US20050289140A1 - Search query processing to provide category-ranked presentation of search results - Google Patents
Search query processing to provide category-ranked presentation of search results Download PDFInfo
- Publication number
- US20050289140A1 US20050289140A1 US11/186,403 US18640305A US2005289140A1 US 20050289140 A1 US20050289140 A1 US 20050289140A1 US 18640305 A US18640305 A US 18640305A US 2005289140 A1 US2005289140 A1 US 2005289140A1
- Authority
- US
- United States
- Prior art keywords
- category
- search
- item
- query
- user
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Abandoned
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
- G06F16/358—Browsing; Visualisation therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/951—Indexing; Web crawling techniques
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S707/00—Data processing: database and file management or data structures
- Y10S707/99931—Database or file accessing
- Y10S707/99933—Query processing, i.e. searching
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S707/00—Data processing: database and file management or data structures
- Y10S707/99931—Database or file accessing
- Y10S707/99937—Sorting
Definitions
- the present invention relates to the field of search engines. More specifically, the invention relates to techniques for facilitating viewing of search results that span multiple item categories, and for locating web pages that include offerings for products and other types of items.
- a videos section of the same web site may contain video biographies of Mark Twain and video adaptations of many of his classic books, while a music section may include compact discs with songs inspired by his writings.
- an auctions section of the same site may contain products offered for sale by third parties that may be of interest to the user, such as Mark Twain memorabilia.
- Another problem in the field of on-line commerce is that of locating a web site from which a particular product can be purchased.
- This problem may arise, for example, when the online merchants known to the consumer do not carry the product of interest.
- the consumer may use an Internet search engine such as ALTAVISTA or EXCITE to search for a web site that sells the product.
- the scope of such a general search is often large enough, however, that only a small fraction of a large number of located web sites actually offer the product for sale.
- the search may include a relatively large number of sites that merely provide reviews, technical support, specifications, or other information about the product of interest. Thus the sites of greatest interest to the consumer are likely to be buried deep within a long list.
- the present invention seeks to overcome these and other problems.
- the present invention provides various features for assisting users in conducting online searches.
- the features may be embodied alone or in combination within a search engine of an online merchant, an Internet search engine, or another type of search system.
- One feature of the invention involves a method for displaying the results of a multiple-category search according to levels of significance of the categories to a user's search query.
- the method can be used to display the results of a search for products or for any other type of item.
- the method involves receiving a search query from a user and identifying, within each of multiple item categories, a set of items that satisfy the query.
- the sets of items are then used to generate, for each of the multiple categories, a score that indicates a level significance or relevance of the category to the search.
- the scores may be based, for example, on the number of hits (items satisfying the query) within each category relative to the total number of items in that category, the popularity levels of items that satisfy the query, or a combination thereof.
- the categories and associated items are then presented to the user in a display order that depends upon the scores—preferably from highest-to-lowest significance.
- Other significance criteria such as a category preference profile of the user, may additionally be used to select the display order.
- other display methods for highlighting the most highly ranked categories may additionally or alternatively be used. The method increases the likelihood that the categories that are of most interest to the user will be presented near the top of the search results listing, or otherwise called to the attention of the user.
- no more that N items e.g., the most highly ranked three items
- each web page located by a crawler program is initially evaluated, according to a set of content-based rules, to generate a score that indicates a likelihood that the web page includes a product offering.
- the scores may additionally be based on other criteria, such as the content of other web pages of the same web site.
- Representations of some or all of the scored web pages are stored in a keyword index that maps keywords to addresses (URLs) of the web pages.
- the keyword index is used by a query server to locate web pages that are both relevant to a user's search query and likely to include a product offering. This may be accomplished, for example, by limiting a scope of the search to web pages having a score that satisfies a particular threshold.
- the above-described features are embodied in combination within a search engine of a host merchant's web site. From this web site, a user can initiate an “All Products” type search that spans multiple product categories.
- the submitted search query is used to identify a set of products that satisfy the query, and a set of web pages that both satisfy the query and that have been determined to likely include product offerings.
- the results of the search are presented using a composite web page which lists at least some of the located products and at least some of the located web pages.
- the products are preferably displayed in conjunction with their respective product categories according to the above-described category ranking and display method.
- FIG. 1 illustrates a system in which users access web site information via the Internet, and illustrates the basic web site components used to implement a search engine that operates in accordance with the present invention.
- FIG. 2 illustrates a sample search tool interface page of the web site.
- FIG. 3 illustrates a sample results page for an All Products search.
- the results include items directly offered for sale by the host merchant web site, items offered for sale by third parties using the host web site as a forum, items offered for sale by on-line merchants affiliated with the host merchant, and items offered for sale by on-line merchants unaffiliated with the host merchant.
- FIG. 4 illustrates a sample results page displaying “Related Products” items associated with on-line merchants who are unaffiliated with the host merchant web site.
- FIG. 5 illustrates the process used to generate the product spider database of FIG. 1 .
- FIG. 6 illustrates the process used to generate a return page in response to an “All Products” search query.
- FIG. 7 illustrates the structure of the Books database of FIG. 1 .
- FIG. 8 illustrates the process used to generate a category relevancy ranking for use by the process of FIG. 6 .
- FIG. 9 illustrates the process used to find All Products search results for both common and uncommon search queries.
- One feature of the present invention involves a method for identifying and displaying product information derived from multiple product categories to a user in response to a query submitted to a search engine by the user.
- Another feature of the invention involves methods for users to search for web pages from which particular products can be purchased.
- these two features are embodied within a common search engine system; as will be apparent, these and other features of the invention can be used independently of one another and may therefore be considered as distinct inventions.
- the term “invention” is used herein to refer collectively to the various inventive features disclosed.
- FIG. 1 illustrates the AMAZON.COM web site 130 , including components used to implement a search engine in accordance with the invention.
- the AMAZON.COM web site includes functionality for allowing users to search, browse, and make purchases from an on-line catalog of book titles, music titles, and other types of items via the Internet 120 . Because the catalog contains millions of items, it is important that the site provide an efficient mechanism for assisting users in locating items.
- the web site 130 includes a web server application 132 (“web server”) that processes user requests received from user computers 110 via the Internet 120 . These requests include queries submitted by users to search the on-line catalog for products.
- the web server 132 records the user transactions, including query submissions, within a query log 136 .
- the web site also includes a query server 140 that processes queries by searching a number of databases 141 - 147 .
- the Books database 141 , Music database 142 , and Videos database 143 include product identifiers for books, musical products, and multimedia products, respectively, that users may purchase directly from the web site 130 .
- the AMAZON.COM web site includes other categories of products sold directly through the web site, such as Electronics and Toys & Games, that are omitted from FIG. 1 in the interest of clarity.
- the Books, Music, and Videos databases 141 - 143 are intended to represent all databases within the web site 130 associated with products marketed directly by the web site merchant.
- the Auctions database 144 of FIG. 1 includes information about third party on-line auctions hosted by the web site 130 .
- the AMAZON.COM web site also hosts fixed-price third party offerings, known as “zShops,” corresponding to a version of an on-line “flea market.”
- the zShops category contains a database analogous to the Auctions database 144 that is omitted from FIG. 1 in the interest of clarity.
- the Auctions database 144 in FIG. 1 is intended to represent all databases within the web site 130 associated with hosting third party transactions.
- the affiliated Merchant databases labeled Software 145 and Electronics 146 include information about software and electronics products, respectively, that are offered for sale on independent web sites affiliated with the host web site 130 .
- the AMAZON.COM web site includes other categories of products sold on independent affiliated web sites, such as Sports & Outdoors and Toys & Games, that are omitted from FIG. 1 in the interest of clarity.
- the Software and Electronics databases 145 , 146 are intended to represent all databases associated with products sold by independent web site merchants affiliated with the web site 130 .
- the Product Spider database 147 includes information about independent web sites, unaffiliated with the host web site 130 , that have been identified as offering products for sale. This database is particularly useful in that it allows the host web-site 130 to help a consumer find product offerings for products that are not sold by the host web site 130 or by affiliated on-line merchants.
- Each of the databases 141 - 147 contain data tables indexed by keyword to facilitate searching in response to queries.
- the division of the product offerings into multiple databases will be referred to as separate “categories.”
- the Product Spider database 147 is one of seven categories displayed in FIG. 1 .
- the web site 130 also includes a database of HTML (Hypertext Markup Language) content that includes, among other things, product information pages that show and describe products associated with the web site 130 .
- HTML Hypertext Markup Language
- the query server 140 includes a category ranking process 150 that prioritizes, by category, the results of searches across all of the various databases 141 - 147 .
- the prioritization scheme is based upon an assessment of the significance of each category to the search query submitted by the user.
- the query server 140 also includes a spell checker 152 for detecting and correcting misspellings in search attempts, and a search tool 154 capable of generating search results from a database (e.g. the Books database 141 ) in response to a query submitted by a user.
- the search tool 154 prioritizes the items within a search result using different criteria depending upon the database used for the search.
- One approach, used for the Product Spider database 147 ranks the search result items through the well known “term frequency inverse document frequency” (TFIDF) approach, in which the weighting applied to each term of a multiple-term query is inversely related to the term's frequency of appearance in the database.
- TTF term frequency inverse document frequency
- the term in a query that appears least often in a database e.g. the Product Spider database 147
- Algorithms for implementing this approach are well known and are commonly available in software development kits associated with commercial search engines such ALTAVISTA and EXCITE.
- the Product Spider database 147 is generated through the use of a web crawler 160 that crawls web sites on the Internet 120 while storing copies of located web pages.
- the output of the web crawler 160 is input to a product score generator 162 that assigns a numerical score (“product score”) to each web page based upon the likelihood that the page offers a product for sale for either online or offline purchase.
- product score a numerical score
- any type of item that can be purchased is considered a “product,” including but not limited to physical goods, services, software, and downloadable content.
- the products may be based on a more narrow definition of what constitutes a product.
- the product score 170 associated with each indexed web page is stored in the Product Spider Database 147 .
- the web page entries could be grouped according to product score (e.g., top third, middle third, bottom third) without actually storing the score values.
- the product scores could be generated and stored on a site-by-site basis rather than on a page-by-page basis.
- Information within the web pages assessed by the product score generator 162 is extracted by an index tool 164 and stored into the Product Spider database 147 .
- the index tool 164 is complimentary to the search tool 154 in that the index tool 164 outputs data in a fully text indexed database format that is searchable by the search tool 154 .
- the index tool 164 could be configured to extract only those keywords that fall within a predefined distance (e.g., 10 words) of indicia of a product offering. This distance can be a fixed distance, or can be selected based on the type of indicia involved (dollar sign, manufacturer name, etc). This variation would tend to produce a keyword index in which the keywords are associated with specific products.
- a similar approach can be used to generate the squib 169 ; for example, the squib could be generated by extracting sentences that immediately precede or follow some indicia of a product offering.
- the Product Spider database 147 is indexed by keyword 166 .
- Each keyword in the database is associated with one or more web pages for which the indexer 164 has determined an association.
- the database includes a URL (“uniform resource locator”) address 168 for locating the web site, a short string of text (a “squib”) 169 extracted from the web site, and a product score value 170 indicative of the likelihood that the web site offers a product for sale.
- the Product Spider database 147 may include information beyond that shown in FIG. 1 .
- Other types of information that may by stored include, for example, a product category (e.g., books, music, video, etc.) ascertained from parsing the web page (or a collection of pages), an age appropriateness indicator (e.g., products appropriate for adults only), a language indicator for the web site (English, Spanish, etc.), product reviews, whether the offers are for new or used products, and whether the products are available on-line, off-line, or both.
- the web site 130 provides users an option to rate the located merchants (e.g., on a scale of 1-5), and to view the ratings entered by other users
- the Product Spider Database 147 may store the merchant ratings data.
- the web server 132 , query server 140 , category ranking process 150 , and database software run on one or more UNIX-based servers and workstations (not shown) of the web site 130 , although other platforms could be used. To accommodate a large number of users, the query server 140 and the databases 141 - 147 may be replicated across multiple machines.
- the web site components invoked during the searching process are collectively referred to herein as a “search engine.”
- the web crawler 160 is preferably running continuously on one or more platforms (not shown) separate from the platforms used for the search engine.
- the product score generator 162 and indexer 164 preferably run on one or more platforms (not shown) separate from those used for the search engine and web crawler 160 .
- FIG. 2 illustrates the general format of a search tool interface page 200 of the host web site 130 that can be used to search for products.
- Users can pursue products using a browse tree interface 210 organized into predetermined categories such as books, music, videos, and auctions.
- users may search for products using a search engine interface 220 .
- Users can perform searches with the search engine interface 220 by typing in the desired information (referred to herein as a “query”) into a query window 230 and then clicking on a search initiation button 240 .
- the user may control the scope of the search with a pulldown window 250 containing multiple categories.
- the search may be limited to any one category through selection of that category from the pulldown menu 250 .
- the user may conduct a broad-based search through selection of an “All Products” option 260 .
- the search engine will present to the user a query results page (or multiple pages linked by hypertext, if the search finds a large number of items) containing a list of items matching the query.
- the search results page includes, for each item found, a hypertext link to additional web pages containing, among other things, product information about the item.
- the query server 140 effectively logically ANDs the query terms together to perform the search. For example, if the user enters the terms “Mark” and “Twain” into the query field 230 of FIG. 2 , the query server 140 will search for and return a list of all items that are associated with both words.
- the search engine seeks to find misspellings within the query by submitting each query term to the spell checker 152 .
- Any of a variety of types of spell checkers may be used for this purpose.
- the spell checker 152 operates as described in U.S. application Ser. No. 09/115,662, filed Jul. 15, 1998, entitled “System and Method for Correcting Spelling Errors in Search Queries,” which is hereby incorporated by reference. If the spell checker 152 determines that a term of the search query may be misspelled, a new term is substituted into the query and the query server 140 completes a new search with the modified query. In this situation the user is notified of the modification made to the query. If no results are found with the modified query, the user is presented with a “no results” page.
- the query server 140 performs searches on the individual terms of the query. In this situation the user is notified of the absence of exact matches, and is informed that the results merely represent close matches.
- the query server 140 applies the query to the database, or databases, corresponding to the search scope selected by the user. For example, if the user has selected the “Books” field from the pulldown menu 250 , the query is only submitted to the Books database 141 .
- the query server 140 applies the query to all of the product databases 141 - 147 .
- the search tool 154 generates results independently from each of the databases 141 - 147 .
- FIG. 3 illustrates the general format of a search results page 300 of the AMAZON.COM web site 130 generated and displayed to the user in response to an “All Products” search on the query “Mark Twain.”
- the results page 300 displays the search results in three separate sections: a “Top Search Results” section 305 , an “Additional Matches” section 350 , and a “Related Products” section 380 .
- the most prominent (i.e. highest) section of the page displays the Top Search Results section 305 .
- This section displays some result items generated from application of the query to the databases directly associated with the web site 130 , that is, to the Books, Music, Videos, and Auctions databases 141 - 144 .
- These results are depicted in FIG. 3 categorized under the Books 310 , Videos 320 , Auctions 330 , and Music 340 headings, and represent products that are available for purchase from the host merchant. For these categories, as many as three items associated with each category are preferably displayed to the user.
- the matching search result items displayed on the All Products search results page 300 are referred to as “top-level” search result items.
- top-level items listed under Books 310 are entitled “Letters From the Earth” 312 , “Following the Equator: A Journey Around the World” 314 , and “Joan of Arc” 316 .
- Each top-level listing includes a hypertext link to product detail pages including information about the associated item.
- Hypertext links ( 318 , 328 , 338 , 348 ) providing access to lists of additional items within the respective categories that match the search query are provided for each category as well. These non-displayed additional items are referred to as “lower-level” search result items.
- the search tool 154 determines whether a matching item qualifies as a top-level, as opposed to bottom level, item using criteria that will be discussed later.
- the display order of the categories within the Top Search Results section 305 is determined by the category ranking process 150 , based upon an assessment of the likely relevance of each category to the search query.
- the category ranking process 150 determined that, for this particular search query, the Books category 310 was likely to be of greatest relevance to the user, followed by the Videos 320 , Auctions 330 , and Music 340 categories.
- An important benefit of this feature is that it reduces the need for users to view or scroll through search results that are not of interest. The manner in which relevance is assessed will be discussed later with the help of FIG. 8 .
- the AMAZON.COM web site includes a much larger number of categories that compete for priority within the Top Search Results section 305 .
- the All Products search results page 300 shown in FIG. 3 displays the Additional Matches section 350 .
- This middle section displays the results generated from application of the query to the affiliated merchant databases, that is, to the Software and Electronics databases 145 , 146 .
- These results are categorized in FIG. 3 under the Software 360 and Electronics 370 headings.
- Within each category up to three items associated with that category are preferably displayed to the user.
- the items “A Horse's Tale” 362 , “Extracts from Adam's Diary” 364 , and “A Visit to Heaven” 366 are the top three matches as assessed by the search tool 154 .
- the search tool 154 assesses whether or not an item qualifies as a top-level item.
- Hypertext links ( 368 , 378 ) to additional matches (i.e. lower-level items) are provided for the respective categories.
- the Electronics heading 370 shown in FIG. 3 like the Music category 340 above, does not display top-level results, but instead includes a link to lower-level results. Again, this presentation is used when the search tool 154 finds matches within the category, but fails to find any matches qualifying for top-level status.
- the order of the categories within the Additional Matches section 350 is determined by the category ranking process 150 , based upon an assessment of the likely relevance of each category to the search query.
- the category ranking process 150 determined that, for this particular search query, the Software category 360 was likely to be of greater relevance to the user than the Electronics category 370 .
- the AMAZON.COM web site includes a large number of categories that compete for priority within the Additional Matches section 350 .
- the results page displays the Related Products section 380 .
- This section displays the search results generated from application of the query to the unaffiliated merchant database, that is, to the Product Spider database 147 . In the preferred embodiment, no top-level results are displayed for this category. Instead, the results are accessible from the All Products search results page 300 via a hypertext link labeled “Related Products” 380 .
- the search of the Product Spider database 147 preferably does not take place simultaneously with the searches of the other databases 141 - 146 . Rather, the Product Spider search is initiated by the user's selection of the Related Products hypertext link 380 , instead of by the user's selection of the search initiation button 240 .
- top three (or more) Product Spider results are displayed on the All Products results page 300 together with the categories in the Top Search Results and Additional Matches sections.
- the display format of the All Products search results page illustrated in FIG. 3 allows a user to very efficiently identify all of the categories of products that are relevant to the query submitted by the user. This efficiency results in part from the limited number of items displayed to the user within each category.
- FIG. 4 illustrates the general format of a Related Products search results page 400 generated in response to the selection of the Related Products hypertext link 380 in FIG. 3 .
- the Product Spider results page 400 displays the query search result items found in the Spider database 147 .
- the results are displayed as hypertext links in order of likelihood of relevance to the search query as assessed by the search tool 154 .
- the search tool 154 assesses the relevance of a multiple-term query to the Product Spider database 147 through inverse document frequency. That is, the weight given to a query term is inversely proportional to the frequency with which it appears in the database. For example, if a user enters the multiple-term query “Mark Twain” into the search engine query field 230 , the term “Twain” is likely to appear far less than the term “Mark” in the Product Spider database 147 . As such, when searching the database 147 the search tool 154 will give far greater weight to the term “Twain” when prioritizing the results for display to the user.
- the search tool 154 further prioritizes the results according to each query term's number of appearances, and location of appearance, within the web page. Appearances in the web page title are given the greatest priority; appearances in the first eight words of the body are given secondary priority; appearances in the subsequent thirty-two words of the body are given tertiary priority; and appearances in the remainder of the body are given lowest priority.
- This priority scheme which is included with the search tool software developer's kit, is adjustable as needed.
- the search tool 154 may use the product score values 170 (indicative of the likelihood that the corresponding web pages contain products available for purchase) stored in the Product Spider Database 147 to assist in the prioritization of the results generated from the Product Spider Database 147 .
- FIG. 4 indicates that applying the above rules to the Product Spider database 147 for the query “Mark Twain” provides five highest ranking results, the top three of which are entitled, “Mark Twain: Wild Humorist of the West” 410 , “Vintage Lifestyles—A visit to Mark Twain's House” 420 , and “Celebrated Jumping Frog of Calavaras County” 430 .
- Each search result item includes a hypertext link to a web page of the unaffiliated merchant associated with the result. For example, selecting the top search result item takes the user to the unaffiliated merchant web site located at the URL: “http://207.98.171.148:80/books/twain.html” 414 .
- Each search result item also includes a short squib 412 derived from the web page during the creation of the Product Spider database 147 .
- the results are generally partitioned so that only the top results are displayed on the first results page.
- the first results page only displays the top five search result items.
- the first page will display substantially more than five items.
- Matches with lower priority are displayed on additional Related Products results pages.
- These pages are accessible in sequential order via a “Next” button 440 , or through a direct access link 442 .
- the user may further refine the search by accessing a refinement link 450 that allows the submission of additional search query terms.
- the Product Spider results page 400 includes a rating 460 for one or more of the displayed result items.
- a rating (three out of five stars) is associated with the fifth result item (Autobiography of Mark Twain) based upon ratings provided by users who have previously interacted with this same on-line merchant.
- the rating information is stored as an entry in the Product Spider database 147 (not shown in FIG. 1 ) corresponding to the on-line merchant's URL.
- the rating information is stored in a separate “ratings” database, indexed by canonical URL.
- the Product Spider results page 400 further includes an option, such as a hypertext link 462 , for the user to rate the merchant.
- the option to rate the merchant preferably exists for every result item displayed on the Product Spider results page 400 , and in all lower level results pages.
- FIG. 4 shows such an option for only one of the five merchants merely for the sake of clarity.
- FIG. 5 illustrates the sequence of steps that are performed to construct and refresh the Product Spider database 147 .
- the web crawler 160 crawls a fraction X of the World Wide Web.
- Web crawling programs which attempt to locate all web pages accessible on the World Wide Web by following hypertext links, are well known in the art.
- the size of the fraction X of the World Wide Web that is crawled in step 510 depends upon the frequency with which the Product Spider database is refreshed.
- the World Wide Web presently contains a sufficiently large number of web pages so as to require an extended period of time for complete crawling. As such, if the Product Spider database 147 is refreshed frequently, only a fraction X of the World Wide Web is crawled between database updates.
- the web pages found through step 510 are passed through a page analyzing step 520 in which the non-content based characters of the web page HTML code (e.g., the typesetting characters, the hypertext link indicators, etc.) are removed.
- the remaining characters correspond to the text-based content of the web page.
- This content is passed to the product score generator 162 which generates a numerical score between 0 and 100 indicative of the likelihood that the source web page offers a product for sale. Scores of 0 and 100 indicate the smallest and largest likelihood, respectively, that the page is offering a product for sale. Loosely speaking, the product score may be thought of as a degree of confidence written as a percentage of absolute certainty.
- a target page may be assessed by analyzing, in addition to the content of the target page itself, the contents of other web pages linked to the target page.
- the analysis may be limited to “neighboring” web pages (i.e., web pages directly accessible via a link on the target page), or it may extend to encompass more remotely accessible web pages (i.e., web pages that are only accessible via a series of links).
- the contributions of other web pages to the assessment of the target page may be weighted such that the influence of a remote page decreases with the number of links between the page and the target page, and/or such that only web pages of the same web site are considered.
- a web site may be analyzed as a single entity.
- the web site assessment may occur by combining the results of a page by page assessment of the web pages within the site, or it may occur by analyzing the web site as a whole.
- the page analyzing step 520 also looks for character strings judged to be inappropriate for users of the host web site 130 . For example, web sites identified as marketing salacious adult content are excluded from the Product Spider database 147 .
- the product score generator 162 first generates a set of confidence parameters designed to assess the degree to which the content-based text of a web page suggests a product is being offered for sale.
- One confidence parameter, “HasOfferingPrice,” quantifies the presence of character strings indicative of offering prices.
- the product score generator 162 parses the page contents looking for character strings indicative of currency, such as “$,” “US$” (for prices in dollars), “£” (for prices in pounds), and “dm” (for prices in Deutschmarks) followed by a string of digits.
- the algorithm also looks for strings indicative of an offering price, such as “price is [ ],” “price: [ ],” “list: [ ],” “regularly: [ ],” “our price is [ ],” “price including standard shipping is [ ],” “cost is [ ],” “on sale now at [ ],” “on sale now for [ ],” and “[ ] for one,” where in each case the square brackets signify a currency indicator followed by a string of numbers.
- the HasOfferingPrice parameter is incrementally increased through a “NoisyOr” operation with a weighting factor.
- the properties of the noisysyOr operation are characterized in Table I, where a variable B (0 ⁇ B ⁇ 1) is noisysyOr'ed against select values of a parameter A (0 ⁇ A ⁇ 1). TABLE I A B noisysyOr(A,B) 1 B 1 3 ⁇ 4 B 3 ⁇ 4 + 1 ⁇ 4B 1 ⁇ 2 B 1 ⁇ 2 + 1 ⁇ 2B 1 ⁇ 4 B 1 ⁇ 4 + 3 ⁇ 4B 0 B B
- the weighting factor used for a particular text pattern for a particular confidence parameter within the noisysyOr operation depends upon the degree of confidence associated with the text pattern.
- Table II provides weighting factors, determined empirically, for the HasOfferingPrice parameter for some example text patterns.
- the parameters H old and H new refer to the HasOfferingPrice parameter before and after, respectively, the noisysyOr operation is applied.
- confidence parameters analogous to HasOfferingPrice, are used to quantify a wide variety of character strings associated with product offerings, including the presence of warranty terms, sales tax information, shipping information, SKU numbers, shopping carts, and click-to-buy options. Each confidence parameter is incremented through the use of the NoisyOr operation and weighting factors in the same manner described above for the HasOfferingPrice parameter.
- the specific character strings and weighting factors used for the confidence parameters are disclosed in Appendices A, B and C of the incorporated-by-reference parent application.
- the product score generator 162 combines the finished set of confidence parameters through a series of nested noisysyOr operations, again using empirical weighting factors, to generate a single product score for the page.
- the specific combinations and weighting factors used to generate the product score are disclosed in Appendix D of the incorporated-by-reference parent application.
- the vast majority of the web pages on the World Wide Web are not associated with product offerings, and as such their corresponding product scores are low. As shown in FIG. 5 , these web pages are excluded from the Product Spider database 147 by a filtering step 530 .
- the filter is simply a threshold number, preferably thirty, that the web page product score must equal or exceed to satisfy the filter. Web pages having a product score below thirty are discarded 532 as inappropriate for the Product Spider database 147 . Typically about 99% of all web pages in the World Wide Web are discarded in this manner. Those pages having product scores satisfying the filter criteria are retained.
- the corresponding URLs are submitted back 540 to the web crawler 160 for a second crawling stage 560 .
- pages may be indexed without regard to their respective product scores.
- the filter comprises multiple ranges of product score values with predetermined minimum and maximum values. For example, four separate databases may be created for web pages having product score values of 20-40, 40-60, 60-80, and 80-100, respectively. In these latter embodiments the product scores may optionally be omitted from the respective databases.
- the URLs from the existing database 147 are submitted 550 to the second crawling stage 560 as well. Duplication between the previous database submissions 550 and the latest web crawl submissions 540 are detected and removed (not shown).
- the second crawling stage 560 shown in FIG. 5 typically requires substantially less time than the first crawling stage 510 , as the number of web pages involved is considerably smaller.
- the results of the second web crawling stage are passed through a second page analyzing stage 570 , wherein product scores are generated anew.
- a second filtering stage 580 pages failing to satisfy the filter are once again discarded 582 .
- Those pages satisfying the second filtering stage 580 are passed in step 590 to the index tool 164 for further processing.
- the second filtering stage 580 preferably uses the same criteria as the first filtering stage 530 .
- the second filtering stage 580 may have either more or less discriminating criteria than the first filtering stage 530 .
- the pages retained after the second filtering stage 580 shown in FIG. 5 are passed to an indexing stage 590 wherein the index tool 164 creates the Product Spider database 147 , fully text indexed by keyword 166 .
- a given web page will contain multiple index keywords distributed throughout its text.
- the database 147 includes, for each keyword 166 , one or more web page addresses 167 with corresponding titles 168 , squibs 169 , and product scores 170 . All of the product scores will necessarily equal or exceed thirty in the preferred embodiment due to the second filtering stage 580 .
- the web page addresses 167 stored in the Product Spider database 147 are preferably “canonicalized” URLs.
- URLs often include one or more strings of characters appended to the addressing information that specify, for example, a particular user ID, session ID, or transaction ID. These characters are not needed for accessing the web page, and are thus preferably discarded, resulting in a “canonical” URL for inclusion in the Product Spider database 147 .
- Techniques for canonicalizing URLs are well known in the art.
- the title 168 entry of the database 147 is preferably duplicated directly from the title used for the web page, as identified by the appropriate HTML tags. If a web page has an inappropriate title, or is missing a title, a new title is inserted into the database 147 as needed on a case by case basis.
- the squib 169 entry of the database 148 is generated automatically by the index tool 164 .
- the squib corresponds to the initial series of words on a web page, up to a preset number of characters set at about two-hundred.
- the squib displays relevant text extracted from the web page corresponding to the products offered for sale on the web page.
- the process illustrated in FIG. 5 may be used to update the Product Spider database 147 as often as desired.
- the Product Spider database 147 is updated every week, more preferably the database is updated every three or four days, and even more preferably it is updated every day.
- the Product Spider database 147 may alternatively be constructed without storing the product scores for each page.
- the database comprises only pages having a product score satisfying predetermined criteria, for example, requiring the product score to equal or exceed thirty (as in the filtering steps 530 , 580 of FIG. 5 ).
- the database comprises multiple indexed tables created without storing the product scores, wherein each table is constructed from web pages having a product score satisfying unique criteria, for example, four separate indexed tables containing pages having product scores from 20-40, 40-60, 60-80 and 80-100, respectively.
- the Product Spider database 147 consists of multiple indexed tables, wherein each table is constructed from web pages that are distinguishable on the basis of some aspect of product offerings (ascertained from parsing the web pages) unrelated to product scores.
- the database 147 consists of separate tables for different categories of goods (e.g., books, music, videos, electronics, software, and toys).
- a separate table is used for products unsuitable for children.
- different tables are constructed for web sites written in different languages (English, Japanese, German, etc.).
- different tables are constructed for on-line and off-line product offerings.
- the page analyzer steps 520 , 570 include searching for character strings judged to be associated with the various predefined categories.
- the user is capable of conducting a more refined search within the Product Spider database 147 .
- the Related Products hypertext link 380 is replaced by a pulldown menu comprising different categories corresponding to the distinctions retained within the Product Spider database 147 (e.g., books, music, video, and toys categories, on-line versus off-line offerings, goods versus services, etc.).
- the Related Products search results page 400 displays search results using TFIDF prioritization as applied to the entire Product Spider database 147 . That is, the results consist of a single list drawn from all of the web pages satisfying the filtering steps 530 , 580 of FIG. 5 (e.g., all web pages having product scores at or above 30).
- the search results are presented as a number of lists, with each list having an independent TFIDF prioritization.
- each of the multiple lists consists of pages satisfying different product score criteria. In one embodiment, these multiple lists are displayed separately.
- the lists are concatenated into one long list.
- the Product Spider search feature has been discussed above in the context of a product-oriented search engine of an on-line merchant.
- the feature may be implemented in other contexts as well. It may be implemented, for example, as part of a “general purpose” web search engine as a user-selected option (e.g. through a pulldown menu or through selection of a “product offering” button).
- users of the AMAZON.COM web site 130 may conduct an All Products search that will generate results for items directly offered for sale by the AMAZON.COM web site (organized into multiple categories), items offered for sale by third parties (Auction and zShop users) using the Amazon web site as a forum, items offered for sale by other on-line merchants affiliated with AMAZON.COM (organized into multiple categories), and items offered for sale by on-line merchants unaffiliated with AMAZON.COM (those within the Product Spider database 147 ).
- the results of such a cross-category search be displayed efficiently.
- FIG. 6 illustrates the sequence of steps that are performed to construct an All Products search results page such as depicted in FIG. 3 .
- a first step 610 the user is prompted to enter a search query to all of the products databases 141 - 147 .
- One approach is illustrated in FIG. 2 , where the user may select the All Products option 260 from the pulldown window 250 .
- a search query to be applied to all of the product databases 141 - 147 .
- One approach is illustrated in FIG. 2 , where the user may enter a query into the query field 230 and select the search initiation button 240 .
- the query is applied to all of the categories comprising the All Products search.
- the query is submitted by the query server 140 via the search tool 154 to a separate database 141 - 147 associated with each category.
- Each product database 141 - 147 is indexed by keyword to facilitate searching by the search tool 154 .
- the query results are returned from each of the product databases 141 - 147 , via the search tool 154 , to the query server 140 .
- the search tool prioritizes the results within each category according to a determination of relevance based upon the query terms.
- the method used by the search tool 154 to prioritize the search result items within a category varies depending upon the nature of the category searched. The prioritization methods used are discussed at length below with the help of FIG. 7 .
- a relevance ranking is generated for each competing category based on an assessment of the relevance of the search query to that category.
- the method used to generate the relevance ranking is discussed in more detail below with the help of FIG. 8 .
- a sixth step 660 the categories are arranged in a display order determined by the results of the category relevance ranking step 650 .
- the primary purpose of this step is to display the categories (and associated search results) deemed to be the most closely related to the search query near the top of the search results page.
- a search results page having the appropriate arrangement is generated for display to the user.
- An example All Products search results page 300 is illustrated in FIG. 3 .
- the approach discussed above displays to the user the top-level search results deemed to be of greatest interest to the user in a manner that is efficient (long lists are avoided), comprehensive (all pertinent categories are included, and links to further results are provided), and clear (the organization and prioritization helps the user quickly comprehend the results).
- the method used to prioritize the search result items within the Product Spider database 147 has already been discussed. Briefly, a database entry is given a higher priority depending upon the number of times a search term appears in the page. Appearances in the web page title, and in text near the beginning of the page, are given higher priority than later appearances. In multiple-term queries, the significance of each term is weighted in a manner inversely proportional to how frequently the term appears in the Product Spider database 147 .
- Search result items within the Auctions database 144 are prioritized based upon the ending time of the auction, with a shorter closing time receiving higher priority than a later closing time.
- the top-level Auctions result items displayed on the All Products search results page correspond to the matching Auctions result items (preferably up to a maximum of three) having the most imminent ending times. Selecting the hypertext link to the lower-level matches 338 provides a list of all of the matching Auctions result items.
- These lower-level results may be sorted by the auction ending times, by the TFIDF relevancy of the search query, by the starting time of the auction, by the present number of bids, or by the highest present bid.
- the method of display of these lower-level results is preferably an option for the user (e.g. via a pulldown menu).
- the top-level search result items are prioritized in the same manner used for the Product Spider database 147 , discussed above.
- the zShops results displayed on an All Products search results page correspond to the zShops matching result items (preferably up to a maximum of three) having the highest TFIDF relevancy.
- Selecting an associated hypertext link to lower-level matches provides a list of all of the matching zShops result items.
- These lower-level results may be sorted by TFIDF relevancy, by the starting or ending date of the zShop, or by the product price.
- the method of display of these lower-level results is preferably an option for the user (e.g. via a pulldown menu).
- the results are prioritized using a more sophisticated approach than those discussed above.
- the assessed relevance of a search result item is based upon the frequency with which the item has been selected in the past during similar queries. The manner in which this is accomplished is now discussed with reference to FIG. 7 .
- FIG. 7 illustrates the structure of the Books database 141 of FIG. 1 .
- the database consists of two tables, a Books Full Text Index 710 and a Books Popularity Score Table 750 .
- the Books Full Text Index 710 contains information, indexed by keyword, for every item in the Books catalog of the web site 130 .
- the Books Popularity Score Table 750 contains information about the subset of books from the Books catalog that users of the web site 130 have recently “selected” during on-line searches.
- the Books Full Text Index 710 is indexed by keyword 712 to facilitate searching by the search tool 154 .
- the comprehensive indexing is created in a manner analogous to that discussed above for the Product Spider database.
- the index tool 164 converts the information from a form organized by item into a form organized by keyword.
- the Books Full Text Index 710 contains one or more item identifiers 714 each of which uniquely identify a book within the on-line catalogue of the host web site 130 .
- the Books Full Text Index 710 associates the keyword “Twain” 720 with eight distinct item identification numbers, each corresponding to a single book. Inspection of FIG. 7 reveals that the term “Twain” is associated with a book corresponding to item identification code 1311302165. This association between a keyword and a book may come from the word appearing in the book's title, in the author's name, or in ancillary text, such as descriptions and third party reviews of the book.
- the Books Popularity Score Table 750 is also indexed by keyword 752 .
- the table 750 contains one or more item identifiers 754 analogous to those of the Full Text Index 710 .
- the table also includes, for each keyword-item pair, a “popularity score” 756 , the meaning of which is discussed below.
- the entries in the Books Popularity Score Table 750 are generated through the actions of users conducting on-line product searches on the web site 130 .
- the user's search query is stored in the query log 136 shown in FIG. 1 .
- the hypertext links selected by the user following the search are also stored, as are the times at which the selections are made.
- a query log parsing processor (not shown in FIG. 1 ) extracts the relevant information and generates the popularity scores 756 stored in the Books Popularity Score Table 750 . The manner in which this is done, together with more details about using popularity scores to facilitate query searches, is described in the U.S. application Ser. No. 09/041,081, filed Mar. 10, 1998, entitled “Identifying the Items Most Relevant to a Current Query Based on Items Selected in Connection with Similar Queries,” which is hereby incorporated by reference.
- the popularity scores 756 of the Books Popularity Scores Table 710 reflect the frequency with which users have selected the corresponding item 754 from query results produced from searches containing the corresponding keyword 752 as a query term.
- FIG. 7 indicates that the item associated with code 2722601080 has been selected by a user one time following a search including the query term “Mark” 760 .
- the item identified with code 4603283881, by comparison, has been selected twenty-two times following searches including the query term “Mark” 760 .
- This latter book has also been selected forty-one times following searches including the query term “Twain” 770 .
- Actions may include, for example, displaying additional information about the item, spending certain amounts of time viewing information about the item, accessing hypertext links within the information about the item, adding the item to a shopping basket, and purchasing the item. All of these actions may be assessed from the query log 136 .
- Different weightings may be associated with different user activities. For example, clicking on an item increments the item's popularity score by one while placing the same item in an on-line “shopping cart” increments its popularity score by fifty.
- the popularity scores 756 are determined by the recent actions of users over a predetermined amount of time, such as a week, ensuring that the scores represent current user preferences.
- the Books Popularity Score Table 750 is constructed by merging the results of a number of intermediate tables corresponding to user actions over adjacent periods of time.
- the query log 136 is parsed once per day to generate a daily intermediate table containing keyword-item pairings and corresponding popularity ratings for that day.
- a new full table 750 is constructed by merging the new intermediate table with the most recent N intermediate tables, where N is a predetermined number.
- the parameter N is selected to equal thirteen for all categories. This creates a full table 750 representing results over a “sliding window” in time fourteen days in duration.
- the number N is selected to be larger for categories that experience low user traffic (e.g. Classical Music) than for categories that experience high user traffic (e.g. Books).
- the popularity scores of the multiple intermediate result tables are weighted equally during the merging into the full table 750 .
- the popularity scores of the multiple intermediate tables are assigned different weightings for merging, with the weightings depending on the times at which the intermediate tables were created.
- the weightings used for merger decrease with increasing age of the intermediate table.
- All Products search results from the Books database 141 are prioritized, for purposes of display on the All Products search results page 300 , based on the popularity scores 756 of the Books Popularity Score Table 750 .
- the search tool 154 prioritizes the search result items based upon each item's popularity score. Referring to FIG. 7 , for example, a search of the Books database 141 for the query term “Mark” 760 would prioritize the three items as 4603283881, 9040356769, and 2722601080, as determined by the three popularity scores 22, 7, and 1, respectively.
- the search tool 154 For multiple-term queries, such as “Mark Twain,” the search tool 154 only returns items having entries in the Books Popularity Score Table 750 under both query terms.
- the query “Mark Twain” would trigger a match for item 4603283881, which is present for both query terms, but not for the other items displayed.
- the popularity scores of each term for that item are combined in some manner to create a query phrase popularity score for that item.
- the query phrase popularity score is the sum of the popularity scores 756 of the component terms. In other embodiments, discussed later, a more complicated combination of the scores is used.
- This prioritization scheme is used to determine the top-level matches that are displayed on the All Products search results page 300 .
- the top-level matches correspond to those items, up to a maximum of three, having the highest popularity scores for the submitted query.
- the three top-level matches 312 , 314 , 316 under the Books category 310 represent the three highest popularity scores for the search phrase “Mark Twain.”
- the top-level matches are ordered on the All Products search results page 300 based on popularity score. Referring to FIG. 3 , the items labeled “Letters from the Earth” 312 , “Following the Equator . . . ” 314 , and “Joan of Arc” 316 , had the first, second, and third highest popularity scores, respectively, in the Books database 141 for the submitted search query.
- the lower-level matches are accessible from the All Products search results page 300 via a hypertext link 318 .
- This link 318 generates a lower-level Books results page that displays both the top-level search result items and lower-level search result items.
- the lower-level search result items from the Books Popularity Score Table 750 that matched the submitted query are displayed most prominently, followed by search result items found only in the Full Text Index 710 .
- the lower-level search result items are displayed according to preset categories unrelated to popularity score 756 .
- the lower-level Books results page may display three separate alphabetized lists: one for books that are immediately available, one for books that must be special ordered, and a third for books that are currently out of print.
- the user is provided with the ability to search the Books Full Text Index 710 based on other criteria as well, such as author, title, and ISBN (International Standard Book Number).
- the Music, Videos, Software, and Electronics databases 142 - 146 are structured in the same manner as the Books database 141 shown in FIG. 7 .
- Each category database consists of a Full Text Index, containing comprehensive information about the products within the category, and a Popularity Score Table containing information about recent search and selection activities by users within the category.
- the Auctions database 144 includes an Auctions Full Text Index but lacks an analog to the Popularity Score Table.
- the search tool 154 Upon initiation of an All Products search query, the search tool 154 returns a prioritized list of search result items, for each category, using the approach discussed above for the Books category. The top matches from this prioritized list (up to a maximum of three) become the “top-level” matches, for each category, for display in the All Products search results page 300 . For these categories 320 , 330 , 340 , 360 , 370 , as for the Books category 310 , lower-level search result items are accessible from the All Products search results page 300 via a hypertext link 328 , 338 , 348 , 368 , 378 .
- the third special circumstance arises when a search query is so unusual that the search tool 154 fails to generate a single match within any of the Popularity Score Tables (or in the Auctions database 144 ). This circumstance is discussed at length after the following section with the help of FIG. 9 .
- the search tool 154 may find matches within the Books Popularity Score Table 750 , but not within the analogous Music Popularity Score Table. In this case, there are no top-level search results available to display on the All Products search results page 300 for the Music category 340 . Indeed, this is what is displayed in FIG. 3 . If the search tool 154 finds at least one lower-level result item (i.e. a match in the Music Full Text Index), a hypertext link 348 to the lower-level results is provided on the All Products search results page 300 . Inspection of FIG.
- the categories themselves compete for priority for display purposes. These competitions between categories, like the ranking of items within each category, are based upon an assessment of the relevance of the search query to each competitor.
- FIG. 8 illustrates the category ranking process 150 used to generate a category relevancy ranking for each competing category in an All Products search.
- the categories involved in an All Products search do not all necessarily compete with one another. Rather, the categories may be divided up into a number of “sets.” Within each set, the member categories compete for priority for display purposes. Categories from different sets do not compete. Different sets might themselves compete for priority, or their arrangements may be predetermined. In different embodiments, the sets may be grouped into “sets of competing sets,” and so on, as needed.
- the categories of the host web site 130 are divided, for purposes of an All Products search, into three sets of categories. These sets are most easily seen through inspection of the All Products search results page 300 in FIG. 3 .
- One set consists of categories that compete for priority within the Top Search Results 305 section of the results page 300 .
- Another set consists of categories that compete for priority within the Additional Matches 350 section of the results page 300 .
- a third set consists of a single category, the Product Spider results that are accessible through the Related Products 380 hypertext link.
- the query server 140 identifies a first set of competing categories.
- the query server 140 may identify, for example, the set of categories competing for display space in the Additional Matches 350 section of the All Products search results page 300 .
- These categories are exemplified in FIG. 1 by the Software and Electronics databases 145 , 146 .
- the query server 140 examines the search results for a first category within the first set.
- the query server 140 may examine, for example, the top-level search result items for the Software category.
- the first column of Table IV provides an example of All Products search result items determined from the Software database 144 for the search query “Mark Twain.”
- the search tool 154 determined that the three best top-level Software Category result items are “A Horses Tail,” “Extracts from Adam's Diary,” and “A Visit to Heaven” (these results are also displayed in FIG. 3 ).
- the number in parenthesis adjacent to each item represents the popularity score for that item (see FIG.
- the query server 140 determines a category “popularity” score indicative of the significance of the query term to the category.
- the category popularity scores are generated from some aspect (e.g., the popularity scores) of the constituent search result items in each category.
- a fourth step 840 the query server 140 repeats the above examination of the All Products search results for another category within the first set.
- a category ranking is created in a fifth step 850 based upon the relative values of those category popularity scores.
- a set of weighting factors is applied to the set of category popularity scores.
- the popularity scores may be influenced by the profile of the user who submits the search query. For example, the complete history of selections made by the user within the host web site 130 may be retained in a database (not shown in FIG. 1 ). This information may be used to adjust the weightings to further individualize the presentation. If the user has made 90% of her prior purchases on the host web site 130 from the Videos database 143 , for example, the Videos category popularity scores may be given greater weight to reflect this individualized history.
- the category popularity score is determined by combining the popularity scores of all matching items found in the category's Popularity Score Table, rather than just the matching items having the three highest popularity scores.
- Other manners of combining top-level result item popularity scores into category popularity scores will be apparent to those skilled in the art.
- FIG. 3 Inspection of FIG. 3 reveals that the Software category 360 “won” the competition against the Electronics category 370 . This is unsurprising considering that the Electronics category 370 does not include any top-level search result items. This indicates that there were no Electronics search result items with popularity scores for the query “Mark Twain,” and the Electronics category popularity score using the above embodiments would equal zero.
- a sixth step 860 the query server 140 identifies another set of competing categories and repeats steps two through five 820 - 850 .
- another set of the host web site 130 consists of those categories competing for display space within the Top Search Results 305 section of the All Products search results page 300 .
- These categories are exemplified in FIG. 1 by the Books, Music, Videos, and Auctions databases 141 , 142 , 143 , 144 .
- Categories in this set are handled in much the same manner as was discussed above for the categories of the previous set.
- the category popularity scores are determined from the constituent top-level item popularity scores using one of several possible approaches, as discussed above.
- the Auctions database 144 does not include popularity scores. Rather, as is discussed above, the highest priority top-level matching results are determined based on the amount of time remaining for each matching item's auction. The category popularity score for the Auctions category is therefore determined in a manner distinct from the other categories.
- the Auctions category popularity score is determined by summing up the number of matching items found by the search tool 154 for the submitted search query within the Auctions database 144 . In another embodiment, the Auctions category popularity score is determined by summing up the number of matching auctions with less than a predetermined amount of time remaining. In yet another embodiment, the Auctions category popularity score is determined by a weighted summation of the number of matching auctions, with the weighting factor for a particular auction determined by the amount of time remaining for that auction. Preferably this weighting factor is inversely proportional to the time remaining for the auction.
- the category popularity scores for all of the categories in a competing set are based upon the number of items matching the submitted search query. In another embodiment, the category popularity scores for all of the categories in a set are based upon the fraction of items in the category that match the submitted search query (i.e., the number of items in the category that match the search query divided by the total number of items in the category).
- category popularity score weighting factors is preferably used to “normalize” the popularity scores between the Auctions and the other categories.
- the Books, Music, Auction, and Videos category 310 , 320 , 330 , 340 popularity scores are weighted equally.
- the Auctions category popularity score is given a weighting three times as a large as the scores of the remaining categories.
- the Auctions category popularity score is given a weighting one-third as large as the scores of the remaining categones.
- All Products search results page 300 depicted in FIG. 3 associates rank with vertical location within a section of a web page
- the web page may indicate priority through the use of a different font size, or a different color, through location within a web page (as in FIG. 3 ), through location on separate web pages, through “framing,” or by display of category relevance scores or ranking (optionally expressed as a percentage, as a number of stars, etc.). Numerous possibilities would be apparent to one skilled in the art.
- the final set of categories utilized by the host web site 130 consists of a single category, the Product Spider results. These results are not displayed on the All Products search results page 300 , but rather are accessible on the page 300 through the Related Products 380 hypertext link. Since this set consists of only one category, there is no competition between categories, and the relevance ranking process of FIG. 8 is not be followed.
- a user searching for journal articles may be provided with a top-level search results page with a limited number of items displayed within each of multiple categories.
- a user searching for court opinions may be provided with results divided into state appellate opinions, federal appellate opinions, etc.
- a user searching for discussion groups may be provided with a search results page with the items arranged by the age of participants, subject matter of the discussion, etc.
- a user searching for recipes may be provided with a search results page with the items arranged by food type.
- a user searching for movie reviews may be provided with a search results page with the items arranged by the nature of the reviewer (syndicated newspaper columnist, amateur reviewer, etc.). Numerous possibilities would be apparent to one skilled in the art.
- the search tool 154 may fail to find matches within the Auctions database 144 or within one or more of the Popularity Score Tables of the other databases 141 , 142 , 143 , 145 , 146 .
- the top-level results will preferably be displayed on the All Products search results page 300 regardless of whether any of the other categories have top-level results (i.e., have matching results in the Popularity Score Tables of their product databases). Similarly, as long as at least one of the non-Auction categories finds matches within the category's Popularity Score Table, those top-level results will preferably be displayed on the All Products search results page 300 regardless of whether any other category found top-level results. If no categories find top-level results, the query server 140 does not generate an All Products “no results” page. Instead, the query server 140 undertakes additional steps in an attempt to generate search results from the query for display on the All Products search results page 300 . The process used by the query server 140 in this endeavor is illustrated in FIG. 9 .
- an All Products search begins with a search of the Auctions database 141 (box 910 ) and a search of the Popularity Score Tables of each of the Books, Music, Videos, Software, and Electronics categories (box 920 ). This is also represented in FIG. 6 by box 630 .
- the query server 140 determines whether any top-level results are returned from any categories (box 915 ). If at least one result is returned, the query server 140 jumps to box 650 in FIG. 6 (box 920 ), and the steps discussed earlier for ranking categories are followed.
- the search tool 154 conducts a search of the Full Text Indexes of each of the Books, Music, Videos, Software, and Electronics categories (box 930 ).
- the query server 140 determines whether any results are returned from any categories (box 925 ). If at least one result is returned, the query server 140 process jumps to box 650 in FIG. 6 (box 920 ), eventually resulting in the generation of an All Products search results page 300 .
- the top-level result items from the Full Text Indexes are determined by TFIDF relevancy score.
- the category popularity score for each category returning results is determined from the number of matching items found for that category.
- the category popularity score is determined by the fraction of items in the category that match the submitted search query (i.e., the number of items in the category that match the search query divided by the total number of items in the category).
- the spell checker 152 attempts to find misspellings within the submitted search query (box 940 ). If the spell checker 152 fails to identify any misspelled query terms (box 945 ), a search “no results page” is generated (box 970 ), notifying the user of the lack of results for the submitted search query. If the spell checker 152 successfully identifies a potentially misspelled query term (box 945 ), the spell checker 152 creates a new query phrase by substituting, for the potentially misspelled word, a word found in a dictionary or lookup table. The search tool 154 then repeats the process from boxes 910 through 935 , as needed, using the new query phrase (box 950 ).
- the query server 140 jumps to box 650 in FIG. 6 (box 920 ) and an All Products search results page 300 is generated using the substituted query.
- the results page 300 notifies the user that the submitted query failed to produce an exact match, and displays the modified query.
- the query server 140 divides the query phrase into multiple single term queries. For example, the submitted four-term query, “Twain Sawyer Becky Thatcher,” which will normally only generate results if all four terms are associated with a single item, is divided into four separate one-term queries, “Twain,” “Sawyer,” “Becky” and “Thatcher.”
- the query processor 140 then repeats the process from boxes 910 through 935 , as needed, one time for each one-term query (box 960 ). Matching result items of the one-term queries compete with one another (e.g., based on popularity score in the Books Popularity Score Table 710 ) in the same manner as the results within a multiple-term query.
- Items A, B, and C in Table V match one, two, and three of the query search terms, respectively.
- Item A would be prioritized first based upon the large popularity score associated with the “Twain”-Item A pairing.
- Each query term is considered to add discriminating value for the purpose of locating items wanted by the user.
- the booster is used to elevate those items containing more discriminating information (i.e. more query terms).
- Item B which matches two query terms, is given a 1,000,000 booster.
- Item C which matches three query terms, is given a 2,000,000 booster. In this way the items that are the closest matches to the full submitted query (Items B and C in this case) are given top priority.
- the three items of Table V would be displayed on the All Products search results page 300 , under the appropriate category, in the order C, B, A, as determined by their respective Item-Query popularity scores.
- the same multi-term booster values are used for searches of the Auctions database 141 and the Full Text Indexes of each category.
- the query server 940 jumps to box 650 and an All Products search results page 300 is generated using the multiple one-term queries.
- the results page 300 notifies the user that the results are merely close matches to the submitted query.
- a search “no results page” is generated (box 970 ), notifying the user of the lack of results for the submitted search query.
Abstract
A search engine system displays the results of a multiple-category search according to levels of relevance of the categories to a user's search query. A query server receives a search query from a user and identifies, within each of multiple item categories, a set of items that satisfy the query. The sets of items are used to generate, for each of the multiple categories, a score that reflects a level significance or relevance of the category to the search. The scores may be based, for example, on the number of hits within each category relative to the total number of items in that category, the popularity levels of items that satisfy the query, a personal profile of the user, or a combination thereof. The categories are then presented to the user, together with the most relevant items within each category, in the order of highest to lowest category relevance.
Description
- This application is a continuation of U.S. application Ser. No. 10/404,992, filed Mar. 31, 2003, which is a continuation of U.S. application Ser. No. 09/528,127, filed Mar. 17, 2000, the disclosure of which is hereby incorporated by reference, which claims the benefit of U.S. Provisional Appl. No. 60/169,570, filed Dec. 8, 1999.
- The present invention relates to the field of search engines. More specifically, the invention relates to techniques for facilitating viewing of search results that span multiple item categories, and for locating web pages that include offerings for products and other types of items.
- In the field of electronic commerce, it is common for online merchants to sell products within many different product-related categories. For example, Amazon.com, Inc., the assignee of the present application, sells products within the categories of books, music, video & DVD, toys & games, electronics, home improvement, and auctions. The predefined categories and associated products are typically presented to users in the form of a browse tree. In addition, many merchants provide a search engine for conducting searches for products.
- One problem commonly encountered by online merchants is the inability to effectively present groups of related products that span the predefined categories. Due to the large number of products and categories, and the organization of the web site, many relationships between products may be difficult for the user to ascertain. For example, suppose a user of a merchant's web site is a fan of the American humorist and author Mark Twain. The user may choose to look for books written by Mark Twain through a browse tree in the book section of an on-line commerce web site. Browsing in this manner is likely to reveal a large number of books authored by, or written about, Mark Twain. The user, however, may be unaware that the web site also sells products other than books that may be of interest to fans of Mark Twain. For example, a videos section of the same web site may contain video biographies of Mark Twain and video adaptations of many of his classic books, while a music section may include compact discs with songs inspired by his writings. Similarly, an auctions section of the same site may contain products offered for sale by third parties that may be of interest to the user, such as Mark Twain memorabilia. Although use of the web site's search engine may reveal some of these additional products, the user typically must review a long list of search results in order to identify the products or categories of interest.
- Another problem in the field of on-line commerce is that of locating a web site from which a particular product can be purchased. This problem may arise, for example, when the online merchants known to the consumer do not carry the product of interest. In such a circumstance the consumer may use an Internet search engine such as ALTAVISTA or EXCITE to search for a web site that sells the product. The scope of such a general search is often large enough, however, that only a small fraction of a large number of located web sites actually offer the product for sale. For example, the search may include a relatively large number of sites that merely provide reviews, technical support, specifications, or other information about the product of interest. Thus the sites of greatest interest to the consumer are likely to be buried deep within a long list.
- The present invention seeks to overcome these and other problems.
- The present invention provides various features for assisting users in conducting online searches. The features may be embodied alone or in combination within a search engine of an online merchant, an Internet search engine, or another type of search system.
- One feature of the invention involves a method for displaying the results of a multiple-category search according to levels of significance of the categories to a user's search query. The method can be used to display the results of a search for products or for any other type of item. In a preferred embodiment, the method involves receiving a search query from a user and identifying, within each of multiple item categories, a set of items that satisfy the query. The sets of items are then used to generate, for each of the multiple categories, a score that indicates a level significance or relevance of the category to the search. The scores may be based, for example, on the number of hits (items satisfying the query) within each category relative to the total number of items in that category, the popularity levels of items that satisfy the query, or a combination thereof.
- The categories and associated items are then presented to the user in a display order that depends upon the scores—preferably from highest-to-lowest significance. Other significance criteria, such as a category preference profile of the user, may additionally be used to select the display order. In addition, other display methods for highlighting the most highly ranked categories may additionally or alternatively be used. The method increases the likelihood that the categories that are of most interest to the user will be presented near the top of the search results listing, or otherwise called to the attention of the user. To assist the user in efficiently viewing a cross section of the located items and their categories, no more that N items (e.g., the most highly ranked three items) within each category are preferably displayed on the initial search results page.
- Another feature of the invention involves a system and methods for assisting users in locating web sites or pages from which user-specified products can be purchased. In a preferred embodiment, each web page located by a crawler program is initially evaluated, according to a set of content-based rules, to generate a score that indicates a likelihood that the web page includes a product offering. The scores may additionally be based on other criteria, such as the content of other web pages of the same web site. Representations of some or all of the scored web pages are stored in a keyword index that maps keywords to addresses (URLs) of the web pages. The keyword index is used by a query server to locate web pages that are both relevant to a user's search query and likely to include a product offering. This may be accomplished, for example, by limiting a scope of the search to web pages having a score that satisfies a particular threshold.
- In one embodiment, the above-described features are embodied in combination within a search engine of a host merchant's web site. From this web site, a user can initiate an “All Products” type search that spans multiple product categories. The submitted search query is used to identify a set of products that satisfy the query, and a set of web pages that both satisfy the query and that have been determined to likely include product offerings. The results of the search are presented using a composite web page which lists at least some of the located products and at least some of the located web pages. The products are preferably displayed in conjunction with their respective product categories according to the above-described category ranking and display method.
- These and other features and advantages of the invention will now be described with reference to the drawings of certain preferred embodiments, which are intended to illustrate and not to limit the invention, and in which:
-
FIG. 1 illustrates a system in which users access web site information via the Internet, and illustrates the basic web site components used to implement a search engine that operates in accordance with the present invention. -
FIG. 2 illustrates a sample search tool interface page of the web site. -
FIG. 3 illustrates a sample results page for an All Products search. The results include items directly offered for sale by the host merchant web site, items offered for sale by third parties using the host web site as a forum, items offered for sale by on-line merchants affiliated with the host merchant, and items offered for sale by on-line merchants unaffiliated with the host merchant. -
FIG. 4 illustrates a sample results page displaying “Related Products” items associated with on-line merchants who are unaffiliated with the host merchant web site. -
FIG. 5 illustrates the process used to generate the product spider database ofFIG. 1 . -
FIG. 6 illustrates the process used to generate a return page in response to an “All Products” search query. -
FIG. 7 illustrates the structure of the Books database ofFIG. 1 . -
FIG. 8 illustrates the process used to generate a category relevancy ranking for use by the process ofFIG. 6 . -
FIG. 9 illustrates the process used to find All Products search results for both common and uncommon search queries. - One feature of the present invention involves a method for identifying and displaying product information derived from multiple product categories to a user in response to a query submitted to a search engine by the user. Another feature of the invention involves methods for users to search for web pages from which particular products can be purchased. In the preferred embodiment, these two features are embodied within a common search engine system; as will be apparent, these and other features of the invention can be used independently of one another and may therefore be considered as distinct inventions. For convenience of description, however, the term “invention” is used herein to refer collectively to the various inventive features disclosed.
- A preferred embodiment and implementation of the invention will now be described with reference to the drawings. The description will reference various details of the invention in the context of AMAZON.COM's web site. These details are set forth in order to illustrate, and not to limit, the invention. The scope of the invention is defined only by the appended claims.
- A. Overview of Web Site and Search Engine
-
FIG. 1 illustrates theAMAZON.COM web site 130, including components used to implement a search engine in accordance with the invention. As is well known in the art of Internet commerce, the AMAZON.COM web site includes functionality for allowing users to search, browse, and make purchases from an on-line catalog of book titles, music titles, and other types of items via theInternet 120. Because the catalog contains millions of items, it is important that the site provide an efficient mechanism for assisting users in locating items. - As shown in
FIG. 1 , theweb site 130 includes a web server application 132 (“web server”) that processes user requests received fromuser computers 110 via theInternet 120. These requests include queries submitted by users to search the on-line catalog for products. Theweb server 132 records the user transactions, including query submissions, within aquery log 136. - The web site also includes a
query server 140 that processes queries by searching a number of databases 141-147. TheBooks database 141,Music database 142, andVideos database 143, include product identifiers for books, musical products, and multimedia products, respectively, that users may purchase directly from theweb site 130. The AMAZON.COM web site includes other categories of products sold directly through the web site, such as Electronics and Toys & Games, that are omitted fromFIG. 1 in the interest of clarity. The Books, Music, and Videos databases 141-143 are intended to represent all databases within theweb site 130 associated with products marketed directly by the web site merchant. - The
Auctions database 144 ofFIG. 1 includes information about third party on-line auctions hosted by theweb site 130. The AMAZON.COM web site also hosts fixed-price third party offerings, known as “zShops,” corresponding to a version of an on-line “flea market.” The zShops category contains a database analogous to theAuctions database 144 that is omitted fromFIG. 1 in the interest of clarity. TheAuctions database 144 inFIG. 1 is intended to represent all databases within theweb site 130 associated with hosting third party transactions. - The Affiliated Merchant databases labeled
Software 145 andElectronics 146 include information about software and electronics products, respectively, that are offered for sale on independent web sites affiliated with thehost web site 130. The AMAZON.COM web site includes other categories of products sold on independent affiliated web sites, such as Sports & Outdoors and Toys & Games, that are omitted fromFIG. 1 in the interest of clarity. The Software andElectronics databases web site 130. - The
Product Spider database 147 includes information about independent web sites, unaffiliated with thehost web site 130, that have been identified as offering products for sale. This database is particularly useful in that it allows the host web-site 130 to help a consumer find product offerings for products that are not sold by thehost web site 130 or by affiliated on-line merchants. - Each of the databases 141-147 contain data tables indexed by keyword to facilitate searching in response to queries. For simplicity, the division of the product offerings into multiple databases will be referred to as separate “categories.” For example, the
Product Spider database 147 is one of seven categories displayed inFIG. 1 . - The
web site 130 also includes a database of HTML (Hypertext Markup Language) content that includes, among other things, product information pages that show and describe products associated with theweb site 130. - The
query server 140 includes acategory ranking process 150 that prioritizes, by category, the results of searches across all of the various databases 141-147. The prioritization scheme is based upon an assessment of the significance of each category to the search query submitted by the user. Thequery server 140 also includes aspell checker 152 for detecting and correcting misspellings in search attempts, and asearch tool 154 capable of generating search results from a database (e.g. the Books database 141) in response to a query submitted by a user. Thesearch tool 154 prioritizes the items within a search result using different criteria depending upon the database used for the search. One approach, used for theProduct Spider database 147, ranks the search result items through the well known “term frequency inverse document frequency” (TFIDF) approach, in which the weighting applied to each term of a multiple-term query is inversely related to the term's frequency of appearance in the database. In other words, the term in a query that appears least often in a database (e.g. the Product Spider database 147) is considered to be the most discriminating term in the query, and thus is given the greatest weight by thesearch tool 154. Algorithms for implementing this approach are well known and are commonly available in software development kits associated with commercial search engines such ALTAVISTA and EXCITE. - The
Product Spider database 147 is generated through the use of aweb crawler 160 that crawls web sites on theInternet 120 while storing copies of located web pages. The output of theweb crawler 160 is input to aproduct score generator 162 that assigns a numerical score (“product score”) to each web page based upon the likelihood that the page offers a product for sale for either online or offline purchase. For purposes of generating the score in the preferred embodiment, any type of item that can be purchased is considered a “product,” including but not limited to physical goods, services, software, and downloadable content. In other embodiments, the products may be based on a more narrow definition of what constitutes a product. For example, by requiring or taking into account whether a web site includes information about shipping, non-physical items can be excluded from consideration or accorded a lesser weight. As depicted inFIG. 1 , theproduct score 170 associated with each indexed web page is stored in theProduct Spider Database 147. Alternatively, the web page entries could be grouped according to product score (e.g., top third, middle third, bottom third) without actually storing the score values. As a further refinement, the product scores could be generated and stored on a site-by-site basis rather than on a page-by-page basis. - Information within the web pages assessed by the
product score generator 162 is extracted by anindex tool 164 and stored into theProduct Spider database 147. In some embodiments, only those web pages for which the product score exceeds a threshold value are indexed by theindex tool 164. Theindex tool 164 is complimentary to thesearch tool 154 in that theindex tool 164 outputs data in a fully text indexed database format that is searchable by thesearch tool 154. Any of a variety of commercially available search engine products, such as those available from ALTAVISTA and EXCITE, may be used to implement the index andsearch tools - As a variation to the above-described method, the
index tool 164 could be configured to extract only those keywords that fall within a predefined distance (e.g., 10 words) of indicia of a product offering. This distance can be a fixed distance, or can be selected based on the type of indicia involved (dollar sign, manufacturer name, etc). This variation would tend to produce a keyword index in which the keywords are associated with specific products. A similar approach can be used to generate thesquib 169; for example, the squib could be generated by extracting sentences that immediately precede or follow some indicia of a product offering. - As noted above, the
Product Spider database 147 is indexed bykeyword 166. Each keyword in the database is associated with one or more web pages for which theindexer 164 has determined an association. For each keyword-web site combination, the database includes a URL (“uniform resource locator”)address 168 for locating the web site, a short string of text (a “squib”) 169 extracted from the web site, and aproduct score value 170 indicative of the likelihood that the web site offers a product for sale. - The
Product Spider database 147 may include information beyond that shown inFIG. 1 . Other types of information that may by stored include, for example, a product category (e.g., books, music, video, etc.) ascertained from parsing the web page (or a collection of pages), an age appropriateness indicator (e.g., products appropriate for adults only), a language indicator for the web site (English, Spanish, etc.), product reviews, whether the offers are for new or used products, and whether the products are available on-line, off-line, or both. Furthermore, in embodiments in which theweb site 130 provides users an option to rate the located merchants (e.g., on a scale of 1-5), and to view the ratings entered by other users, theProduct Spider Database 147 may store the merchant ratings data. - The
web server 132,query server 140,category ranking process 150, and database software run on one or more UNIX-based servers and workstations (not shown) of theweb site 130, although other platforms could be used. To accommodate a large number of users, thequery server 140 and the databases 141-147 may be replicated across multiple machines. The web site components invoked during the searching process are collectively referred to herein as a “search engine.” Theweb crawler 160 is preferably running continuously on one or more platforms (not shown) separate from the platforms used for the search engine. Theproduct score generator 162 andindexer 164 preferably run on one or more platforms (not shown) separate from those used for the search engine andweb crawler 160. -
FIG. 2 illustrates the general format of a searchtool interface page 200 of thehost web site 130 that can be used to search for products. Users can pursue products using abrowse tree interface 210 organized into predetermined categories such as books, music, videos, and auctions. Alternatively, users may search for products using asearch engine interface 220. Users can perform searches with thesearch engine interface 220 by typing in the desired information (referred to herein as a “query”) into aquery window 230 and then clicking on asearch initiation button 240. The user may control the scope of the search with apulldown window 250 containing multiple categories. The search may be limited to any one category through selection of that category from thepulldown menu 250. Alternatively, the user may conduct a broad-based search through selection of an “All Products”option 260. - If the query is submitted to a single category, the search engine will present to the user a query results page (or multiple pages linked by hypertext, if the search finds a large number of items) containing a list of items matching the query. The search results page includes, for each item found, a hypertext link to additional web pages containing, among other things, product information about the item.
- For multiple-term queries, the
query server 140 effectively logically ANDs the query terms together to perform the search. For example, if the user enters the terms “Mark” and “Twain” into thequery field 230 ofFIG. 2 , thequery server 140 will search for and return a list of all items that are associated with both words. - If the search fails to find a single matching item, the search engine seeks to find misspellings within the query by submitting each query term to the
spell checker 152. Any of a variety of types of spell checkers may be used for this purpose. In one embodiment, thespell checker 152 operates as described in U.S. application Ser. No. 09/115,662, filed Jul. 15, 1998, entitled “System and Method for Correcting Spelling Errors in Search Queries,” which is hereby incorporated by reference. If thespell checker 152 determines that a term of the search query may be misspelled, a new term is substituted into the query and thequery server 140 completes a new search with the modified query. In this situation the user is notified of the modification made to the query. If no results are found with the modified query, the user is presented with a “no results” page. - If no results are found that contain all of the query terms in a multiple-term query, the
query server 140 performs searches on the individual terms of the query. In this situation the user is notified of the absence of exact matches, and is informed that the results merely represent close matches. - A more complete discussion of the processing of misspelled, or otherwise unusual, query terms is reserved until later with the help of
FIG. 9 . - When the user submits a query from the
search engine interface 220 ofFIG. 2 to theweb site 130, thequery server 140 applies the query to the database, or databases, corresponding to the search scope selected by the user. For example, if the user has selected the “Books” field from thepulldown menu 250, the query is only submitted to theBooks database 141. - If the user has selected the “All Products”
field 260 ofFIG. 2 from thepulldown menu 250, thequery server 140 applies the query to all of the product databases 141-147. Thesearch tool 154 generates results independently from each of the databases 141-147. -
FIG. 3 illustrates the general format of a search resultspage 300 of theAMAZON.COM web site 130 generated and displayed to the user in response to an “All Products” search on the query “Mark Twain.” Theresults page 300 displays the search results in three separate sections: a “Top Search Results”section 305, an “Additional Matches”section 350, and a “Related Products”section 380. - As shown in
FIG. 3 , the most prominent (i.e. highest) section of the page displays the TopSearch Results section 305. This section displays some result items generated from application of the query to the databases directly associated with theweb site 130, that is, to the Books, Music, Videos, and Auctions databases 141-144. These results are depicted inFIG. 3 categorized under theBooks 310,Videos 320,Auctions 330, andMusic 340 headings, and represent products that are available for purchase from the host merchant. For these categories, as many as three items associated with each category are preferably displayed to the user. The matching search result items displayed on the All Products search resultspage 300 are referred to as “top-level” search result items. For example, the top-level items listed underBooks 310 are entitled “Letters From the Earth” 312, “Following the Equator: A Journey Around the World” 314, and “Joan of Arc” 316. Each top-level listing includes a hypertext link to product detail pages including information about the associated item. Hypertext links (318, 328, 338, 348) providing access to lists of additional items within the respective categories that match the search query are provided for each category as well. These non-displayed additional items are referred to as “lower-level” search result items. Thesearch tool 154 determines whether a matching item qualifies as a top-level, as opposed to bottom level, item using criteria that will be discussed later. - For the
Music category 340, no top-level search result items are displayed on the All Products search resultspage 300 in theFIG. 3 example. Instead, only a link to lower-level items 338 is included. This format is used when thesearch tool 154 finds matches within a category, but none of the matches qualify as a top-level item. - In accordance with one feature of the invention, the display order of the categories within the Top
Search Results section 305 is determined by thecategory ranking process 150, based upon an assessment of the likely relevance of each category to the search query. Thus, inFIG. 3 , thecategory ranking process 150 determined that, for this particular search query, theBooks category 310 was likely to be of greatest relevance to the user, followed by theVideos 320,Auctions 330, andMusic 340 categories. An important benefit of this feature is that it reduces the need for users to view or scroll through search results that are not of interest. The manner in which relevance is assessed will be discussed later with the help ofFIG. 8 . - Although only four categories are depicted (for purposes of clarity) in
FIGS. 1 and 3 for products directly associated with theweb site 130, the AMAZON.COM web site includes a much larger number of categories that compete for priority within the TopSearch Results section 305. - Immediately below the Top
Search Results section 305, the All Products search resultspage 300 shown inFIG. 3 displays theAdditional Matches section 350. This middle section displays the results generated from application of the query to the affiliated merchant databases, that is, to the Software andElectronics databases FIG. 3 under theSoftware 360 andElectronics 370 headings. Within each category up to three items associated with that category are preferably displayed to the user. For example, in the Software category the items “A Horse's Tale” 362, “Extracts from Adam's Diary” 364, and “A Visit to Heaven” 366 are the top three matches as assessed by thesearch tool 154. As noted above, thesearch tool 154 assesses whether or not an item qualifies as a top-level item. Hypertext links (368, 378) to additional matches (i.e. lower-level items) are provided for the respective categories. - The Electronics heading 370 shown in
FIG. 3 , like theMusic category 340 above, does not display top-level results, but instead includes a link to lower-level results. Again, this presentation is used when thesearch tool 154 finds matches within the category, but fails to find any matches qualifying for top-level status. - The order of the categories within the
Additional Matches section 350 is determined by thecategory ranking process 150, based upon an assessment of the likely relevance of each category to the search query. Thus, inFIG. 3 , thecategory ranking process 150 determined that, for this particular search query, theSoftware category 360 was likely to be of greater relevance to the user than theElectronics category 370. Although only two categories are depicted (for purposes of clarity) for the affiliated merchant databases inFIGS. 1 and 3 , the AMAZON.COM web site includes a large number of categories that compete for priority within theAdditional Matches section 350. - Immediately below the
Additional Matches section 350, the results page displays theRelated Products section 380. This section displays the search results generated from application of the query to the unaffiliated merchant database, that is, to theProduct Spider database 147. In the preferred embodiment, no top-level results are displayed for this category. Instead, the results are accessible from the All Products search resultspage 300 via a hypertext link labeled “Related Products” 380. The search of theProduct Spider database 147 preferably does not take place simultaneously with the searches of the other databases 141-146. Rather, the Product Spider search is initiated by the user's selection of the RelatedProducts hypertext link 380, instead of by the user's selection of thesearch initiation button 240. - In another embodiment, the top three (or more) Product Spider results are displayed on the All Products results
page 300 together with the categories in the Top Search Results and Additional Matches sections. - The display format of the All Products search results page illustrated in
FIG. 3 allows a user to very efficiently identify all of the categories of products that are relevant to the query submitted by the user. This efficiency results in part from the limited number of items displayed to the user within each category. -
FIG. 4 illustrates the general format of a Related Products search resultspage 400 generated in response to the selection of the RelatedProducts hypertext link 380 inFIG. 3 . The Product Spider resultspage 400 displays the query search result items found in theSpider database 147. The results are displayed as hypertext links in order of likelihood of relevance to the search query as assessed by thesearch tool 154. - As noted previously, the
search tool 154 assesses the relevance of a multiple-term query to theProduct Spider database 147 through inverse document frequency. That is, the weight given to a query term is inversely proportional to the frequency with which it appears in the database. For example, if a user enters the multiple-term query “Mark Twain” into the searchengine query field 230, the term “Twain” is likely to appear far less than the term “Mark” in theProduct Spider database 147. As such, when searching thedatabase 147 thesearch tool 154 will give far greater weight to the term “Twain” when prioritizing the results for display to the user. Thesearch tool 154 further prioritizes the results according to each query term's number of appearances, and location of appearance, within the web page. Appearances in the web page title are given the greatest priority; appearances in the first eight words of the body are given secondary priority; appearances in the subsequent thirty-two words of the body are given tertiary priority; and appearances in the remainder of the body are given lowest priority. This priority scheme, which is included with the search tool software developer's kit, is adjustable as needed. - Furthermore, as discussed below, the
search tool 154 may use the product score values 170 (indicative of the likelihood that the corresponding web pages contain products available for purchase) stored in theProduct Spider Database 147 to assist in the prioritization of the results generated from theProduct Spider Database 147. -
FIG. 4 indicates that applying the above rules to theProduct Spider database 147 for the query “Mark Twain” provides five highest ranking results, the top three of which are entitled, “Mark Twain: Wild Humorist of the West” 410, “Vintage Lifestyles—A visit to Mark Twain's House” 420, and “Celebrated Jumping Frog of Calavaras County” 430. Each search result item includes a hypertext link to a web page of the unaffiliated merchant associated with the result. For example, selecting the top search result item takes the user to the unaffiliated merchant web site located at the URL: “http://207.98.171.148:80/books/twain.html” 414. Each search result item also includes ashort squib 412 derived from the web page during the creation of theProduct Spider database 147. - Since the number of matches found for a search query can be quite large, the results are generally partitioned so that only the top results are displayed on the first results page. In
FIG. 4 , for example, the first results page only displays the top five search result items. Typically the first page will display substantially more than five items. Matches with lower priority are displayed on additional Related Products results pages. These pages are accessible in sequential order via a “Next” button 440, or through adirect access link 442. Furthermore, the user may further refine the search by accessing arefinement link 450 that allows the submission of additional search query terms. - In one embodiment, the Product Spider results
page 400 includes arating 460 for one or more of the displayed result items. InFIG. 4 , a rating (three out of five stars) is associated with the fifth result item (Autobiography of Mark Twain) based upon ratings provided by users who have previously interacted with this same on-line merchant. In one embodiment, the rating information is stored as an entry in the Product Spider database 147 (not shown inFIG. 1 ) corresponding to the on-line merchant's URL. In another embodiment, the rating information is stored in a separate “ratings” database, indexed by canonical URL. In these embodiments, the Product Spider resultspage 400 further includes an option, such as ahypertext link 462, for the user to rate the merchant. The option to rate the merchant preferably exists for every result item displayed on the Product Spider resultspage 400, and in all lower level results pages.FIG. 4 shows such an option for only one of the five merchants merely for the sake of clarity. - Additional details regarding the presentation of the Product Spider results page are provided below following the description of
FIG. 5 . - B. Method for Generating a Product Spider Database
-
FIG. 5 illustrates the sequence of steps that are performed to construct and refresh theProduct Spider database 147. Instep 510, theweb crawler 160 crawls a fraction X of the World Wide Web. Web crawling programs, which attempt to locate all web pages accessible on the World Wide Web by following hypertext links, are well known in the art. The size of the fraction X of the World Wide Web that is crawled instep 510 depends upon the frequency with which the Product Spider database is refreshed. The World Wide Web presently contains a sufficiently large number of web pages so as to require an extended period of time for complete crawling. As such, if theProduct Spider database 147 is refreshed frequently, only a fraction X of the World Wide Web is crawled between database updates. - As shown in
FIG. 5 , the web pages found throughstep 510 are passed through apage analyzing step 520 in which the non-content based characters of the web page HTML code (e.g., the typesetting characters, the hypertext link indicators, etc.) are removed. The remaining characters correspond to the text-based content of the web page. This content is passed to theproduct score generator 162 which generates a numerical score between 0 and 100 indicative of the likelihood that the source web page offers a product for sale. Scores of 0 and 100 indicate the smallest and largest likelihood, respectively, that the page is offering a product for sale. Loosely speaking, the product score may be thought of as a degree of confidence written as a percentage of absolute certainty. - The analysis is conducted on a page by page basis, with each web page being assessed independently. In an alternative embodiment, a target page may be assessed by analyzing, in addition to the content of the target page itself, the contents of other web pages linked to the target page. The analysis may be limited to “neighboring” web pages (i.e., web pages directly accessible via a link on the target page), or it may extend to encompass more remotely accessible web pages (i.e., web pages that are only accessible via a series of links). In these embodiments, the contributions of other web pages to the assessment of the target page may be weighted such that the influence of a remote page decreases with the number of links between the page and the target page, and/or such that only web pages of the same web site are considered.
- In yet another embodiment, a web site may be analyzed as a single entity. The web site assessment may occur by combining the results of a page by page assessment of the web pages within the site, or it may occur by analyzing the web site as a whole.
- The
page analyzing step 520 also looks for character strings judged to be inappropriate for users of thehost web site 130. For example, web sites identified as marketing salacious adult content are excluded from theProduct Spider database 147. - 1. Score Generation Process
- To produce a product score, the
product score generator 162 first generates a set of confidence parameters designed to assess the degree to which the content-based text of a web page suggests a product is being offered for sale. One confidence parameter, “HasOfferingPrice,” quantifies the presence of character strings indicative of offering prices. To create the HasOfferingPrice parameter, theproduct score generator 162 parses the page contents looking for character strings indicative of currency, such as “$,” “US$” (for prices in dollars), “£” (for prices in pounds), and “dm” (for prices in Deutschmarks) followed by a string of digits. The algorithm also looks for strings indicative of an offering price, such as “price is [ ],” “price: [ ],” “list: [ ],” “regularly: [ ],” “our price is [ ],” “price including standard shipping is [ ],” “cost is [ ],” “on sale now at [ ],” “on sale now for [ ],” and “[ ] for one,” where in each case the square brackets signify a currency indicator followed by a string of numbers. Each time a character string denotative of an offering price is found, the HasOfferingPrice parameter is incrementally increased through a “NoisyOr” operation with a weighting factor. - The NoisyOr operation is an analog variant of the binary OR operation, where NoisyOr(A,B)=A+B−(A×B), where A and B are between 0 and 1, inclusive. The properties of the NoisyOr operation are characterized in Table I, where a variable B (0≦B≦1) is NoisyOr'ed against select values of a parameter A (0≦A≦1).
TABLE I A B NoisyOr(A,B) 1 B 1 ¾ B ¾ + ¼B ½ B ½ + ½B ¼ B ¼ + ¾B 0 B B - If A=1, the output is 1 regardless of the value of B, as expected for an OR operation. If A=0, the output is B regardless of the value of B. For intermediate values of A, the output is A summed with a fraction of B. The resultant output is always equal to or larger than the larger of the two inputs, but never bigger than 1.
- The weighting factor used for a particular text pattern for a particular confidence parameter within the NoisyOr operation depends upon the degree of confidence associated with the text pattern. Table II provides weighting factors, determined empirically, for the HasOfferingPrice parameter for some example text patterns. In Table II the parameters Hold and Hnew refer to the HasOfferingPrice parameter before and after, respectively, the NoisyOr operation is applied.
TABLE II Text Pattern Weighting Resulting NoisyOr “your price is []” 0.9 Hnew = NoisyOr(Hold,0.9) “price: [] for one” 0.6 Hnew = NoisyOr(Hold,0.6) “price is [] per person” 0.4 Hnew = NoisyOr(Hold,0.4) “[] /person” 0.2 Hnew = NoisyOr(Hold,0.2) “[] for one” 0.1 Hnew = NoisyOr(Hold,0.1) - Inspection of Table II indicates that the character string “your price is [ ]” is believed to be a very good predictor of web pages offering products for sale. The character string “[ ] for one,” on the other hand, is believed to be a relatively weak predictor.
- Other confidence parameters, analogous to HasOfferingPrice, are used to quantify a wide variety of character strings associated with product offerings, including the presence of warranty terms, sales tax information, shipping information, SKU numbers, shopping carts, and click-to-buy options. Each confidence parameter is incremented through the use of the NoisyOr operation and weighting factors in the same manner described above for the HasOfferingPrice parameter. The specific character strings and weighting factors used for the confidence parameters are disclosed in Appendices A, B and C of the incorporated-by-reference parent application.
- The
product score generator 162 combines the finished set of confidence parameters through a series of nested NoisyOr operations, again using empirical weighting factors, to generate a single product score for the page. The specific combinations and weighting factors used to generate the product score are disclosed in Appendix D of the incorporated-by-reference parent application. - 2. Second Analysis Stare
- In practice, the vast majority of the web pages on the World Wide Web are not associated with product offerings, and as such their corresponding product scores are low. As shown in
FIG. 5 , these web pages are excluded from theProduct Spider database 147 by afiltering step 530. The filter is simply a threshold number, preferably thirty, that the web page product score must equal or exceed to satisfy the filter. Web pages having a product score below thirty are discarded 532 as inappropriate for theProduct Spider database 147. Typically about 99% of all web pages in the World Wide Web are discarded in this manner. Those pages having product scores satisfying the filter criteria are retained. The corresponding URLs are submitted back 540 to theweb crawler 160 for asecond crawling stage 560. - In other embodiments, such as those in which the index is also used to provide a general purpose web search engine, pages may be indexed without regard to their respective product scores. In still other embodiments, the filter comprises multiple ranges of product score values with predetermined minimum and maximum values. For example, four separate databases may be created for web pages having product score values of 20-40, 40-60, 60-80, and 80-100, respectively. In these latter embodiments the product scores may optionally be omitted from the respective databases.
- If the Product Spider database is not being constructed for the first time, but rather is being updated, then the URLs from the existing
database 147 are submitted 550 to thesecond crawling stage 560 as well. Duplication between theprevious database submissions 550 and the latestweb crawl submissions 540 are detected and removed (not shown). - The
second crawling stage 560 shown inFIG. 5 typically requires substantially less time than thefirst crawling stage 510, as the number of web pages involved is considerably smaller. The results of the second web crawling stage are passed through a secondpage analyzing stage 570, wherein product scores are generated anew. In asecond filtering stage 580, pages failing to satisfy the filter are once again discarded 582. Those pages satisfying thesecond filtering stage 580 are passed instep 590 to theindex tool 164 for further processing. - The
second filtering stage 580 preferably uses the same criteria as thefirst filtering stage 530. In an alternative embodiment, thesecond filtering stage 580 may have either more or less discriminating criteria than thefirst filtering stage 530. - 3. Construction of the Product Spider Database
- The pages retained after the
second filtering stage 580 shown inFIG. 5 are passed to anindexing stage 590 wherein theindex tool 164 creates theProduct Spider database 147, fully text indexed bykeyword 166. A given web page will contain multiple index keywords distributed throughout its text. Theindex tool 164 converts the information from a form organized by URL into a form organized by keyword. Schematically, theindex tool 164 reorganizes the set of multiple pages (Pagem, where m=1 to M) containing multiple Keywords (Wordn, where n=1 to N) such that Page1(ΣnWordn), Page2(ΣnWordn), . . . , PageM(ΣnWordn) is converted into Word1(ΣmPagem), Word2(ΣmPagem), . . . , WordN(ΣmPagem). - As shown in
FIG. 1 , thedatabase 147 includes, for eachkeyword 166, one or more web page addresses 167 withcorresponding titles 168, squibs 169, and product scores 170. All of the product scores will necessarily equal or exceed thirty in the preferred embodiment due to thesecond filtering stage 580. - The web page addresses 167 stored in the
Product Spider database 147 are preferably “canonicalized” URLs. URLs often include one or more strings of characters appended to the addressing information that specify, for example, a particular user ID, session ID, or transaction ID. These characters are not needed for accessing the web page, and are thus preferably discarded, resulting in a “canonical” URL for inclusion in theProduct Spider database 147. Techniques for canonicalizing URLs are well known in the art. - The
title 168 entry of thedatabase 147 is preferably duplicated directly from the title used for the web page, as identified by the appropriate HTML tags. If a web page has an inappropriate title, or is missing a title, a new title is inserted into thedatabase 147 as needed on a case by case basis. - The
squib 169 entry of the database 148 is generated automatically by theindex tool 164. The squib corresponds to the initial series of words on a web page, up to a preset number of characters set at about two-hundred. In another embodiment, the squib displays relevant text extracted from the web page corresponding to the products offered for sale on the web page. - The process illustrated in
FIG. 5 may be used to update theProduct Spider database 147 as often as desired. In a preferred embodiment, theProduct Spider database 147 is updated every week, more preferably the database is updated every three or four days, and even more preferably it is updated every day. - As indicated above, the
Product Spider database 147 may alternatively be constructed without storing the product scores for each page. In one embodiment, for example, the database comprises only pages having a product score satisfying predetermined criteria, for example, requiring the product score to equal or exceed thirty (as in the filtering steps 530, 580 ofFIG. 5 ). In another alternative embodiment, the database comprises multiple indexed tables created without storing the product scores, wherein each table is constructed from web pages having a product score satisfying unique criteria, for example, four separate indexed tables containing pages having product scores from 20-40, 40-60, 60-80 and 80-100, respectively. - In another embodiment, the
Product Spider database 147 consists of multiple indexed tables, wherein each table is constructed from web pages that are distinguishable on the basis of some aspect of product offerings (ascertained from parsing the web pages) unrelated to product scores. In one embodiment, for example, thedatabase 147 consists of separate tables for different categories of goods (e.g., books, music, videos, electronics, software, and toys). In another embodiment, a separate table is used for products unsuitable for children. In still another embodiment, different tables are constructed for web sites written in different languages (English, Japanese, German, etc.). In yet another embodiment, different tables are constructed for on-line and off-line product offerings. Under these embodiments, the page analyzer steps 520, 570 include searching for character strings judged to be associated with the various predefined categories. - By constructing the
Product Spider database 147 out of different tables having distinguishing characteristics, or retaining the equivalent information within one big table, the user is capable of conducting a more refined search within theProduct Spider database 147. In one embodiment, for example, the RelatedProducts hypertext link 380 is replaced by a pulldown menu comprising different categories corresponding to the distinctions retained within the Product Spider database 147 (e.g., books, music, video, and toys categories, on-line versus off-line offerings, goods versus services, etc.). - The Related Products search results page 400 (
FIG. 4 ) displays search results using TFIDF prioritization as applied to the entireProduct Spider database 147. That is, the results consist of a single list drawn from all of the web pages satisfying the filtering steps 530, 580 ofFIG. 5 (e.g., all web pages having product scores at or above 30). In another embodiment, the search results are presented as a number of lists, with each list having an independent TFIDF prioritization. In this embodiment, each of the multiple lists consists of pages satisfying different product score criteria. In one embodiment, these multiple lists are displayed separately. In an alternative embodiment, the lists are concatenated into one long list. This latter embodiment is illustrated in Table III, where an All Products search of thehost web site 130 generates two Product Spider results lists, an “A” list for web pages having product scores at or above eighty, and a “B” list for web pages having product scores below eighty but at or above thirty. The A List is presented first (on multiple pages if it is long), and the B List is concatenated onto the end of the A List. This bifurcation attempts to provide the user first with those pages most likely to be offering a product for sale. The B list may optionally be generated only if the number of items in the A list falls below a threshold number, such as five, or if the user requests to view (e.g. via selection of a hypertext link) the B list.TABLE III Lists Criteria A List Product Score ≧ 80 B List 80 > Product Score ≧ 30 - The Product Spider search feature has been discussed above in the context of a product-oriented search engine of an on-line merchant. The feature may be implemented in other contexts as well. It may be implemented, for example, as part of a “general purpose” web search engine as a user-selected option (e.g. through a pulldown menu or through selection of a “product offering” button).
- C. Method for Ranking Categories in an All Products Search
- As noted above, users of the
AMAZON.COM web site 130 may conduct an All Products search that will generate results for items directly offered for sale by the AMAZON.COM web site (organized into multiple categories), items offered for sale by third parties (Auction and zShop users) using the Amazon web site as a forum, items offered for sale by other on-line merchants affiliated with AMAZON.COM (organized into multiple categories), and items offered for sale by on-line merchants unaffiliated with AMAZON.COM (those within the Product Spider database 147). With such a large number of categories involved, it is advantageous that the results of such a cross-category search be displayed efficiently. In particular, it is desirable that the search results of most relevance to the user be displayed so that the user does not need to wade through a long list of irrelevant search results or click through a long series of hypertext links to find the results of greatest interest. -
FIG. 6 illustrates the sequence of steps that are performed to construct an All Products search results page such as depicted inFIG. 3 . In afirst step 610, the user is prompted to enter a search query to all of the products databases 141-147. One approach is illustrated inFIG. 2 , where the user may select theAll Products option 260 from thepulldown window 250. - In a
second step 620 shown inFIG. 6 , the user submits a search query to be applied to all of the product databases 141-147. One approach is illustrated inFIG. 2 , where the user may enter a query into thequery field 230 and select thesearch initiation button 240. - In a
third step 630 shown inFIG. 6 , the query is applied to all of the categories comprising the All Products search. As illustrated inFIG. 1 , the query is submitted by thequery server 140 via thesearch tool 154 to a separate database 141-147 associated with each category. Each product database 141-147 is indexed by keyword to facilitate searching by thesearch tool 154. - In a
fourth step 640 shown inFIG. 6 , the query results are returned from each of the product databases 141-147, via thesearch tool 154, to thequery server 140. The search tool prioritizes the results within each category according to a determination of relevance based upon the query terms. The method used by thesearch tool 154 to prioritize the search result items within a category varies depending upon the nature of the category searched. The prioritization methods used are discussed at length below with the help ofFIG. 7 . - In a
fifth step 650, a relevance ranking is generated for each competing category based on an assessment of the relevance of the search query to that category. The method used to generate the relevance ranking is discussed in more detail below with the help ofFIG. 8 . - In a
sixth step 660, the categories are arranged in a display order determined by the results of the categoryrelevance ranking step 650. The primary purpose of this step is to display the categories (and associated search results) deemed to be the most closely related to the search query near the top of the search results page. In afinal step 670, a search results page having the appropriate arrangement is generated for display to the user. An example All Products search resultspage 300 is illustrated inFIG. 3 . The approach discussed above displays to the user the top-level search results deemed to be of greatest interest to the user in a manner that is efficient (long lists are avoided), comprehensive (all pertinent categories are included, and links to further results are provided), and clear (the organization and prioritization helps the user quickly comprehend the results). - 1. Prioritizing Search Result Items Within Each Category
- The method used to prioritize the search result items within the
Product Spider database 147 has already been discussed. Briefly, a database entry is given a higher priority depending upon the number of times a search term appears in the page. Appearances in the web page title, and in text near the beginning of the page, are given higher priority than later appearances. In multiple-term queries, the significance of each term is weighted in a manner inversely proportional to how frequently the term appears in theProduct Spider database 147. - Search result items within the
Auctions database 144 are prioritized based upon the ending time of the auction, with a shorter closing time receiving higher priority than a later closing time. The top-level Auctions result items displayed on the All Products search results page (seeFIG. 3 ) correspond to the matching Auctions result items (preferably up to a maximum of three) having the most imminent ending times. Selecting the hypertext link to the lower-level matches 338 provides a list of all of the matching Auctions result items. These lower-level results may be sorted by the auction ending times, by the TFIDF relevancy of the search query, by the starting time of the auction, by the present number of bids, or by the highest present bid. The method of display of these lower-level results is preferably an option for the user (e.g. via a pulldown menu). - For databases associated with the AMAZON.COM zShops (fixed-price offerings by third parties—not shown in
FIG. 1 ), the top-level search result items are prioritized in the same manner used for theProduct Spider database 147, discussed above. Thus the zShops results displayed on an All Products search results page correspond to the zShops matching result items (preferably up to a maximum of three) having the highest TFIDF relevancy. Selecting an associated hypertext link to lower-level matches provides a list of all of the matching zShops result items. These lower-level results may be sorted by TFIDF relevancy, by the starting or ending date of the zShop, or by the product price. The method of display of these lower-level results is preferably an option for the user (e.g. via a pulldown menu). - For items within the categories of goods sold directly by the host web site (i.e. goods from the Books, Music, and
Videos databases Electronics databases 145, 146) the results are prioritized using a more sophisticated approach than those discussed above. For these categories, the assessed relevance of a search result item is based upon the frequency with which the item has been selected in the past during similar queries. The manner in which this is accomplished is now discussed with reference toFIG. 7 . -
FIG. 7 illustrates the structure of theBooks database 141 ofFIG. 1 . The database consists of two tables, a BooksFull Text Index 710 and a Books Popularity Score Table 750. The BooksFull Text Index 710 contains information, indexed by keyword, for every item in the Books catalog of theweb site 130. The Books Popularity Score Table 750 contains information about the subset of books from the Books catalog that users of theweb site 130 have recently “selected” during on-line searches. - The Books
Full Text Index 710 is indexed bykeyword 712 to facilitate searching by thesearch tool 154. The comprehensive indexing is created in a manner analogous to that discussed above for the Product Spider database. Briefly, theindex tool 164 converts the information from a form organized by item into a form organized by keyword. Thus, for eachkeyword 712, the BooksFull Text Index 710 contains one ormore item identifiers 714 each of which uniquely identify a book within the on-line catalogue of thehost web site 130. - The Books
Full Text Index 710, for example, associates the keyword “Twain” 720 with eight distinct item identification numbers, each corresponding to a single book. Inspection ofFIG. 7 reveals that the term “Twain” is associated with a book corresponding toitem identification code 1311302165. This association between a keyword and a book may come from the word appearing in the book's title, in the author's name, or in ancillary text, such as descriptions and third party reviews of the book. - The Books Popularity Score Table 750 is also indexed by
keyword 752. For eachkeyword 752, the table 750 contains one ormore item identifiers 754 analogous to those of theFull Text Index 710. The table also includes, for each keyword-item pair, a “popularity score” 756, the meaning of which is discussed below. - The entries in the Books Popularity Score Table 750 are generated through the actions of users conducting on-line product searches on the
web site 130. When a user conducts a search within thehost web site 130, the user's search query is stored in the query log 136 shown inFIG. 1 . The hypertext links selected by the user following the search are also stored, as are the times at which the selections are made. Through parsing of thequery log 136, the user's actions may be followed in great detail. A query log parsing processor (not shown inFIG. 1 ) extracts the relevant information and generates the popularity scores 756 stored in the Books Popularity Score Table 750. The manner in which this is done, together with more details about using popularity scores to facilitate query searches, is described in the U.S. application Ser. No. 09/041,081, filed Mar. 10, 1998, entitled “Identifying the Items Most Relevant to a Current Query Based on Items Selected in Connection with Similar Queries,” which is hereby incorporated by reference. - The popularity scores 756 of the Books Popularity Scores Table 710 reflect the frequency with which users have selected the
corresponding item 754 from query results produced from searches containing thecorresponding keyword 752 as a query term. For example,FIG. 7 indicates that the item associated with code 2722601080 has been selected by a user one time following a search including the query term “Mark” 760. The item identified withcode 4603283881, by comparison, has been selected twenty-two times following searches including the query term “Mark” 760. This latter book has also been selected forty-one times following searches including the query term “Twain” 770. - Different actions by a user may be used to qualify as a “selection” for purposes of determining the
popularity score 756. Actions may include, for example, displaying additional information about the item, spending certain amounts of time viewing information about the item, accessing hypertext links within the information about the item, adding the item to a shopping basket, and purchasing the item. All of these actions may be assessed from thequery log 136. - Different weightings may be associated with different user activities. In one embodiment, for example, clicking on an item increments the item's popularity score by one while placing the same item in an on-line “shopping cart” increments its popularity score by fifty.
- Preferably the popularity scores 756 are determined by the recent actions of users over a predetermined amount of time, such as a week, ensuring that the scores represent current user preferences. Preferably the Books Popularity Score Table 750 is constructed by merging the results of a number of intermediate tables corresponding to user actions over adjacent periods of time. The
query log 136 is parsed once per day to generate a daily intermediate table containing keyword-item pairings and corresponding popularity ratings for that day. Each day, a new full table 750 is constructed by merging the new intermediate table with the most recent N intermediate tables, where N is a predetermined number. The parameter N is selected to equal thirteen for all categories. This creates a full table 750 representing results over a “sliding window” in time fourteen days in duration. In another embodiment, the number N is selected to be larger for categories that experience low user traffic (e.g. Classical Music) than for categories that experience high user traffic (e.g. Books). - The popularity scores of the multiple intermediate result tables are weighted equally during the merging into the full table 750. In an alternative embodiment, the popularity scores of the multiple intermediate tables are assigned different weightings for merging, with the weightings depending on the times at which the intermediate tables were created. In one such embodiment, the weightings used for merger decrease with increasing age of the intermediate table.
- If a book falls out of fashion, and thus is not selected within the time period stored in the present version of the
Books database 141, the book will fail to appear in the associated Books Popularity Score Table 710. If a book has been selected within the relevant time period, it will contain one entry in the table 710 for every query term utilized by users prior to selecting the book over that time period. During a later period the same book may have completelydifferent keyword 712 entries if the users selecting the book utilized different search query terms to find it. - All Products search results from the
Books database 141 are prioritized, for purposes of display on the All Products search resultspage 300, based on the popularity scores 756 of the Books Popularity Score Table 750. - For single-term queries, the
search tool 154 prioritizes the search result items based upon each item's popularity score. Referring toFIG. 7 , for example, a search of theBooks database 141 for the query term “Mark” 760 would prioritize the three items as 4603283881, 9040356769, and 2722601080, as determined by the threepopularity scores - For multiple-term queries, such as “Mark Twain,” the
search tool 154 only returns items having entries in the Books Popularity Score Table 750 under both query terms. InFIG. 7 , for example, the query “Mark Twain” would trigger a match foritem 4603283881, which is present for both query terms, but not for the other items displayed. When multiple items match all of the terms of a multiple-term query, the popularity scores of each term for that item are combined in some manner to create a query phrase popularity score for that item. In one embodiment, the query phrase popularity score is the sum of the popularity scores 756 of the component terms. In other embodiments, discussed later, a more complicated combination of the scores is used. - This prioritization scheme is used to determine the top-level matches that are displayed on the All Products search results
page 300. The top-level matches correspond to those items, up to a maximum of three, having the highest popularity scores for the submitted query. For example, inFIG. 3 the three top-level matches 312, 314, 316 under theBooks category 310 represent the three highest popularity scores for the search phrase “Mark Twain.” Furthermore, the top-level matches are ordered on the All Products search resultspage 300 based on popularity score. Referring toFIG. 3 , the items labeled “Letters from the Earth” 312, “Following the Equator . . . ” 314, and “Joan of Arc” 316, had the first, second, and third highest popularity scores, respectively, in theBooks database 141 for the submitted search query. - For the
Books category 310 of an All Products search, the lower-level matches are accessible from the All Products search resultspage 300 via ahypertext link 318. Thislink 318 generates a lower-level Books results page that displays both the top-level search result items and lower-level search result items. - In one embodiment, the lower-level search result items from the Books Popularity Score Table 750 that matched the submitted query are displayed most prominently, followed by search result items found only in the
Full Text Index 710. In an alternative embodiment, the lower-level search result items are displayed according to preset categories unrelated topopularity score 756. In one embodiment, for example, the lower-level Books results page may display three separate alphabetized lists: one for books that are immediately available, one for books that must be special ordered, and a third for books that are currently out of print. Preferably the user is provided with the ability to search the BooksFull Text Index 710 based on other criteria as well, such as author, title, and ISBN (International Standard Book Number). - The Music, Videos, Software, and Electronics databases 142-146 are structured in the same manner as the
Books database 141 shown inFIG. 7 . Each category database consists of a Full Text Index, containing comprehensive information about the products within the category, and a Popularity Score Table containing information about recent search and selection activities by users within the category. TheAuctions database 144, on the other hand, includes an Auctions Full Text Index but lacks an analog to the Popularity Score Table. - Upon initiation of an All Products search query, the
search tool 154 returns a prioritized list of search result items, for each category, using the approach discussed above for the Books category. The top matches from this prioritized list (up to a maximum of three) become the “top-level” matches, for each category, for display in the All Products search resultspage 300. For thesecategories Books category 310, lower-level search result items are accessible from the All Products search resultspage 300 via ahypertext link - There are three special circumstances in which prioritization of one or more of the Books, Music, Video, Software, and Electronics categories is not based on popularity scores. First, when a category is newly introduced it takes some time (e.g. two weeks) for the corresponding Popularity Score Table to accumulate sufficient user selections to result in useful popularity scores. During this “introductory period” the top-level All Products search result items are prioritized using the TFIDF relevancy approach discussed earlier.
- The second special circumstance arises when a new product line is introduced within a category. In this situation popularity scores may be plentiful for the category as a whole, but scores for the newly released product line will necessarily be lacking. In order to assist users in finding the new product line during this “transition period,” the top-level All Products search result items for that particular category are prioritized using the TFIDF relevancy approach discussed earlier.
- The third special circumstance arises when a search query is so unusual that the
search tool 154 fails to generate a single match within any of the Popularity Score Tables (or in the Auctions database 144). This circumstance is discussed at length after the following section with the help ofFIG. 9 . - For some All Products search queries, the
search tool 154 may find matches within the Books Popularity Score Table 750, but not within the analogous Music Popularity Score Table. In this case, there are no top-level search results available to display on the All Products search resultspage 300 for theMusic category 340. Indeed, this is what is displayed inFIG. 3 . If thesearch tool 154 finds at least one lower-level result item (i.e. a match in the Music Full Text Index), ahypertext link 348 to the lower-level results is provided on the All Products search resultspage 300. Inspection ofFIG. 3 reveals that this is the case for the Music andElectronics categories page 300. - 2. Ranking Categories Based on Relevance
- Once the
search tool 154 has generated search results from each of the categories, the categories themselves compete for priority for display purposes. These competitions between categories, like the ranking of items within each category, are based upon an assessment of the relevance of the search query to each competitor. -
FIG. 8 illustrates thecategory ranking process 150 used to generate a category relevancy ranking for each competing category in an All Products search. The categories involved in an All Products search do not all necessarily compete with one another. Rather, the categories may be divided up into a number of “sets.” Within each set, the member categories compete for priority for display purposes. Categories from different sets do not compete. Different sets might themselves compete for priority, or their arrangements may be predetermined. In different embodiments, the sets may be grouped into “sets of competing sets,” and so on, as needed. - The categories of the
host web site 130 are divided, for purposes of an All Products search, into three sets of categories. These sets are most easily seen through inspection of the All Products search resultspage 300 inFIG. 3 . One set consists of categories that compete for priority within theTop Search Results 305 section of theresults page 300. Another set consists of categories that compete for priority within theAdditional Matches 350 section of theresults page 300. A third set consists of a single category, the Product Spider results that are accessible through theRelated Products 380 hypertext link. - Referring to
FIG. 8 , in afirst step 810 thequery server 140 identifies a first set of competing categories. Thequery server 140 may identify, for example, the set of categories competing for display space in theAdditional Matches 350 section of the All Products search resultspage 300. These categories are exemplified inFIG. 1 by the Software andElectronics databases - In a
second step 820, thequery server 140 examines the search results for a first category within the first set. Thequery server 140 may examine, for example, the top-level search result items for the Software category. The first column of Table IV provides an example of All Products search result items determined from theSoftware database 144 for the search query “Mark Twain.” Thesearch tool 154 determined that the three best top-level Software Category result items are “A Horses Tail,” “Extracts from Adam's Diary,” and “A Visit to Heaven” (these results are also displayed inFIG. 3 ). The number in parenthesis adjacent to each item represents the popularity score for that item (seeFIG. 7 and associated discussion) for the search query “Mark Twain.”TABLE IV Software Flowers & Gifts Packaged Travel A Horse's Tail (59) Mark Twain Riverboat Autumn in the Ozarks Extracts from Adam's (57) (61) Diary (20) On the Trail of Mark Bermuda (4) A Visit to Heaven (11) Twain (13) Europe - Atlantic Crossing (1) - In a
third step 830, thequery server 140 determines a category “popularity” score indicative of the significance of the query term to the category. The category popularity scores are generated from some aspect (e.g., the popularity scores) of the constituent search result items in each category. In one embodiment, the category popularity score is determined by summing the constituent top-level result item popularity scores. Applying this approach to the Software results shown in Table IV leads to a category popularity score of 90 (=59+20+1). - In a
fourth step 840, thequery server 140 repeats the above examination of the All Products search results for another category within the first set. For example, the second and third columns of Table IV show search results for Flowers & Gifts and Packaged Travel categories (not shown inFIGS. 1 and 3 ) that compete with the Software category for priority within theAdditional Matches 350 section of the All Products search resultspage 300. Determining category popularity scores using the approach discussed above results in scores of 70 (=57+13) for Flowers & Gifts and 66 (=61+4+1) for Packaged Travel. - After category popularity scores have been determined for all members of the set, a category ranking is created in a
fifth step 850 based upon the relative values of those category popularity scores. The rankings are determined through a comparison of each category popularity result. For example, using the category popularity results determined above, the Software (score=90), Flowers & Gifts (score=70), and Packaged Travel (score=66) categories would be ranked first, second, and third, respectively. The categories would be arranged (boxes FIG. 6 ) appropriately based on this ranking. That is, the Software category results would be displayed in the most prominent manner, the Flowers & Gifts category results would be displayed in the next most prominent manner, and the Packaged Travel category results would be displayed in the least prominent manner of the three. - In another embodiment, a set of weighting factors is applied to the set of category popularity scores. Such weighting factors may be used to help or hinder particular categories as desired. For example, if it was decided that during the holiday season the Flowers & Gifts category should be provided a competitive advantage, that category may be given a weighting factor of two, with each of the remaining categories having a weighting factor of one. With such a weighting set, the Software (score=1×90=90), Flowers & Gifts (score=2×70=140), and Packaged Travel (score=1×66=66) categories would now be ranked second, first, and third, respectively. These weighting factors may be influenced by the profile of the user who submitted the search query. Furthermore, the popularity scores may be influenced by the profile of the user who submits the search query. For example, the complete history of selections made by the user within the
host web site 130 may be retained in a database (not shown inFIG. 1 ). This information may be used to adjust the weightings to further individualize the presentation. If the user has made 90% of her prior purchases on thehost web site 130 from theVideos database 143, for example, the Videos category popularity scores may be given greater weight to reflect this individualized history. - In another embodiment, the category popularity score is determined by taking the mean value of the constituent top-level result item popularity scores. Applying this approach to the results shown in Table IV leads to category scores of 30 (=90/3) for Software, 35 (=70/2) for Flowers & Gifts, and 22 (=66/3) for Packaged Travel. Thus, under this approach, the Flowers & Gifts category results would be displayed in the most prominent position.
- In another embodiment, the category popularity score is determined by taking the highest value of the constituent top-level result item popularity scores. Applying this approach to the results shown in Table IV leads to category scores of 59 for Software, 57 for Flowers & Gifts, and 61 (=66/3) for Packaged Travel. Thus, under this approach, the Packaged Travel category results would be displayed in the most prominent position.
- In still another embodiment, the category popularity score is determined by combining the popularity scores of all matching items found in the category's Popularity Score Table, rather than just the matching items having the three highest popularity scores. Other manners of combining top-level result item popularity scores into category popularity scores will be apparent to those skilled in the art.
- Inspection of
FIG. 3 reveals that theSoftware category 360 “won” the competition against theElectronics category 370. This is unsurprising considering that theElectronics category 370 does not include any top-level search result items. This indicates that there were no Electronics search result items with popularity scores for the query “Mark Twain,” and the Electronics category popularity score using the above embodiments would equal zero. - Referring to
FIG. 8 , in asixth step 860 thequery server 140 identifies another set of competing categories and repeats steps two through five 820-850. For example, another set of thehost web site 130 consists of those categories competing for display space within theTop Search Results 305 section of the All Products search resultspage 300. These categories are exemplified inFIG. 1 by the Books, Music, Videos, andAuctions databases - Categories in this set are handled in much the same manner as was discussed above for the categories of the previous set. For the Books, Music, and
Videos categories - A complication arises, however, since the Auctions category uses a completely different approach than the other categories in determining the top-level search result items. In particular, the
Auctions database 144 does not include popularity scores. Rather, as is discussed above, the highest priority top-level matching results are determined based on the amount of time remaining for each matching item's auction. The category popularity score for the Auctions category is therefore determined in a manner distinct from the other categories. - In one embodiment, the Auctions category popularity score is determined by summing up the number of matching items found by the
search tool 154 for the submitted search query within theAuctions database 144. In another embodiment, the Auctions category popularity score is determined by summing up the number of matching auctions with less than a predetermined amount of time remaining. In yet another embodiment, the Auctions category popularity score is determined by a weighted summation of the number of matching auctions, with the weighting factor for a particular auction determined by the amount of time remaining for that auction. Preferably this weighting factor is inversely proportional to the time remaining for the auction. - In one embodiment, the category popularity scores for all of the categories in a competing set (including the non-Auction categories) are based upon the number of items matching the submitted search query. In another embodiment, the category popularity scores for all of the categories in a set are based upon the fraction of items in the category that match the submitted search query (i.e., the number of items in the category that match the search query divided by the total number of items in the category).
- The use of category popularity score weighting factors, discussed above, is preferably used to “normalize” the popularity scores between the Auctions and the other categories. In one embodiment, the Books, Music, Auction, and
Videos category - Inspection of
FIG. 3 reveals that theBooks category 310 is the highest on the All Products search resultspage 300. This indicates that theBooks category 310 “won” the competition against the Videos, Auctions, andMusic categories - Although the All Products search results
page 300 depicted inFIG. 3 associates rank with vertical location within a section of a web page, there are other ways in which the results of a category may be given greater priority. For example, the web page may indicate priority through the use of a different font size, or a different color, through location within a web page (as inFIG. 3 ), through location on separate web pages, through “framing,” or by display of category relevance scores or ranking (optionally expressed as a percentage, as a number of stars, etc.). Numerous possibilities would be apparent to one skilled in the art. - The final set of categories utilized by the
host web site 130 consists of a single category, the Product Spider results. These results are not displayed on the All Products search resultspage 300, but rather are accessible on thepage 300 through theRelated Products 380 hypertext link. Since this set consists of only one category, there is no competition between categories, and the relevance ranking process ofFIG. 8 is not be followed. - The above discussion describes the category ranking process in the context of searching for product offerings. The process is also applicable to other contexts. For example, a user searching for journal articles may be provided with a top-level search results page with a limited number of items displayed within each of multiple categories. A user searching for court opinions may be provided with results divided into state appellate opinions, federal appellate opinions, etc. A user searching for discussion groups may be provided with a search results page with the items arranged by the age of participants, subject matter of the discussion, etc. A user searching for recipes may be provided with a search results page with the items arranged by food type. A user searching for movie reviews may be provided with a search results page with the items arranged by the nature of the reviewer (syndicated newspaper columnist, amateur reviewer, etc.). Numerous possibilities would be apparent to one skilled in the art.
- 3. Handling Uncommon Search Queries
- For uncommon search queries, the
search tool 154 may fail to find matches within theAuctions database 144 or within one or more of the Popularity Score Tables of theother databases - If results are found within the
Auctions database 144, the top-level results will preferably be displayed on the All Products search resultspage 300 regardless of whether any of the other categories have top-level results (i.e., have matching results in the Popularity Score Tables of their product databases). Similarly, as long as at least one of the non-Auction categories finds matches within the category's Popularity Score Table, those top-level results will preferably be displayed on the All Products search resultspage 300 regardless of whether any other category found top-level results. If no categories find top-level results, thequery server 140 does not generate an All Products “no results” page. Instead, thequery server 140 undertakes additional steps in an attempt to generate search results from the query for display on the All Products search resultspage 300. The process used by thequery server 140 in this endeavor is illustrated inFIG. 9 . - As represented in
FIG. 9 , an All Products search begins with a search of the Auctions database 141 (box 910) and a search of the Popularity Score Tables of each of the Books, Music, Videos, Software, and Electronics categories (box 920). This is also represented inFIG. 6 bybox 630. Thequery server 140 determines whether any top-level results are returned from any categories (box 915). If at least one result is returned, thequery server 140 jumps tobox 650 inFIG. 6 (box 920), and the steps discussed earlier for ranking categories are followed. - If no top-level results are returned as determined by
box 915, thesearch tool 154 conducts a search of the Full Text Indexes of each of the Books, Music, Videos, Software, and Electronics categories (box 930). Thequery server 140 determines whether any results are returned from any categories (box 925). If at least one result is returned, thequery server 140 process jumps tobox 650 inFIG. 6 (box 920), eventually resulting in the generation of an All Products search resultspage 300. In this case, the top-level result items from the Full Text Indexes (preferably up to a maximum of three) are determined by TFIDF relevancy score. In one embodiment the category popularity score for each category returning results is determined from the number of matching items found for that category. In another embodiment the category popularity score is determined by the fraction of items in the category that match the submitted search query (i.e., the number of items in the category that match the search query divided by the total number of items in the category). - If no results are returned as determined by
box 935, thespell checker 152 attempts to find misspellings within the submitted search query (box 940). If thespell checker 152 fails to identify any misspelled query terms (box 945), a search “no results page” is generated (box 970), notifying the user of the lack of results for the submitted search query. If thespell checker 152 successfully identifies a potentially misspelled query term (box 945), thespell checker 152 creates a new query phrase by substituting, for the potentially misspelled word, a word found in a dictionary or lookup table. Thesearch tool 154 then repeats the process fromboxes 910 through 935, as needed, using the new query phrase (box 950). If the new query phrase generates results as assessed inboxes query server 140 jumps tobox 650 inFIG. 6 (box 920) and an All Products search resultspage 300 is generated using the substituted query. Theresults page 300 notifies the user that the submitted query failed to produce an exact match, and displays the modified query. - If no results are returned for the modified query as determined by
box 955, thequery server 140 divides the query phrase into multiple single term queries. For example, the submitted four-term query, “Twain Sawyer Becky Thatcher,” which will normally only generate results if all four terms are associated with a single item, is divided into four separate one-term queries, “Twain,” “Sawyer,” “Becky” and “Thatcher.” Thequery processor 140 then repeats the process fromboxes 910 through 935, as needed, one time for each one-term query (box 960). Matching result items of the one-term queries compete with one another (e.g., based on popularity score in the Books Popularity Score Table 710) in the same manner as the results within a multiple-term query. In this situation, however, a priority “booster” is added to the popularity scores of result items that match two or more of the search terms. The size of the booster is given by 1,000,000×(N−1), where N is the number of terms matched. Table V illustrates an example of the use of boosters for the four-term search query given above.TABLE V Matching Item-Term Multiple Term Item-Query Item: Terms: Popularity Scores Booster Popularity Score A Twain 3566 0 3566 B Twain 1140 1,000,000 1,001,332 Sawyer 192 C Twain 20 2,000,000 2,000,040 Becky 8 Thatcher 12 - Items A, B, and C in Table V match one, two, and three of the query search terms, respectively. Without the use of boosters, Item A would be prioritized first based upon the large popularity score associated with the “Twain”-Item A pairing. Each query term, however, is considered to add discriminating value for the purpose of locating items wanted by the user. Thus the booster is used to elevate those items containing more discriminating information (i.e. more query terms). Item B, which matches two query terms, is given a 1,000,000 booster. Item C, which matches three query terms, is given a 2,000,000 booster. In this way the items that are the closest matches to the full submitted query (Items B and C in this case) are given top priority. Thus the three items of Table V would be displayed on the All Products search results
page 300, under the appropriate category, in the order C, B, A, as determined by their respective Item-Query popularity scores. - In order to maintain proper normalization, the same multi-term booster values are used for searches of the
Auctions database 141 and the Full Text Indexes of each category. - If any of the one-term queries generate matching results (box 965), the
query server 940 jumps tobox 650 and an All Products search resultspage 300 is generated using the multiple one-term queries. Theresults page 300 notifies the user that the results are merely close matches to the submitted query. - If no results are returned for the multiple one-term queries (box 965), a search “no results page” is generated (box 970), notifying the user of the lack of results for the submitted search query.
- Although this invention has been described in terms of certain preferred embodiments, other embodiments that are apparent to those of ordinary skill in the art are also within the scope of this invention. Accordingly, the scope of the present invention is intended to be defined only by reference to the appended claims.
- In the claims, which follow, reference characters used to denote process steps are provided for convenience of description only, and not to imply a particular order for performing the steps.
Claims (19)
1. A computer-implemented method for processing a search query specified by a user so as to personalize a presentation of search results, the method comprising:
identifying, within each of a plurality of categories, one or more items that are responsive to the search query (“responsive items”);
calculating a respective category score for each of the plurality of categories, said category score reflecting a predicted level of affinity the user has for the respective category as determined from a preexisting profile of the user, said category score further reflecting a degree to which the respective category is relevant to the search query; and
presenting the plurality of categories to the user, together with associated responsive items, in an order which depends upon the category scores;
whereby the order in which the categories are presented is personalized for the user.
2. The method of claim 1 , wherein the preexisting profile reflects item purchases made by the user.
3. The method of claim 1 , wherein the category score for a given category depends upon item popularity scores of responsive items within the given category.
4. The method of claim 1 , wherein the category score for a given category is based further upon a number of responsive items falling within the given category relative to a total number of items falling within the given category.
5. A web page generated according to the method of claim 1 represented within a computer, said web page displaying responsive items arranged by category in said order.
6. A server system that processes search queries according to the method of claim 1 .
7. A computer readable medium which stores a computer program that embodies the method of claim 1 .
8. The method of claim 1 , wherein the search query specified by the user comprises a query term, and wherein calculating the category scores comprises taking into consideration item selections of prior users who have submitted search queries that included said query term.
9. The method of claim 1 , wherein the predicted level of affinity the user has for the respective category is based, at least in part, on item selections of the user.
10. A computer-implemented method of processing search queries, comprising:
receiving a search query submitted by a user, said search query comprising a query term;
identifying a plurality of item categories that each include one or more items that are responsive to the search query;
for each of the plurality of item categories, determining a respective category score that reflects a degree to which the respective item category is relevant to the search query, each category score being dependent upon prior item selection actions of users who have submitted a search query that includes said query term; and
selecting an order in which to present the plurality of item categories to the user in a response to the search query such that the order is dependent upon the categories.
11. The method of claim 10 , further comprising generating the category scores, at least in-part, by recording search query submissions and associated item selection actions of a plurality of users, and programmatically analyzing said search query submissions and associated item selection actions.
12. The method of claim 10 , wherein each category score is reflective of how frequently an item falling in the respective item category has been selected by a user who has submitted a search query containing said query term.
13. The method of claim 10 , wherein selecting an order in which to present the plurality of item categories further comprises taking into consideration category affinities of the user.
14. The method of claim 10 , further comprising, for at least a first item category of said plurality of item categories:
identifying items in said first item category that are responsive to the search query (“responsive items”);
determining item scores for particular responsive items, each item score being dependent upon item selection actions of users who have submitted a search query that includes said query term; and
using the item scores to select a display order in which to display the responsive items.
15. The method of claim 14 , wherein each item score reflects how frequently the corresponding responsive item has been selected by a user who has submitted a search query that includes said query term.
16. The method of claim 14 , further comprising taking at least some of the item scores into consideration in generating the category score for the first item category.
17. The method of claim 10 , further comprising generating a search results page which lists the plurality of item categories in said order, and which lists, for each item category, one or more responsive items.
18. A search results page generated according to the method of claim 17 represented in computer storage.
19. A computer readable medium which stores a computer program that embodies the method of claim 10.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US11/186,403 US20050289140A1 (en) | 1999-12-08 | 2005-07-21 | Search query processing to provide category-ranked presentation of search results |
Applications Claiming Priority (4)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US16957099P | 1999-12-08 | 1999-12-08 | |
US52812700A | 2000-03-17 | 2000-03-17 | |
US10/404,992 US6963867B2 (en) | 1999-12-08 | 2003-03-31 | Search query processing to provide category-ranked presentation of search results |
US11/186,403 US20050289140A1 (en) | 1999-12-08 | 2005-07-21 | Search query processing to provide category-ranked presentation of search results |
Related Parent Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US10/404,992 Continuation US6963867B2 (en) | 1999-12-08 | 2003-03-31 | Search query processing to provide category-ranked presentation of search results |
Publications (1)
Publication Number | Publication Date |
---|---|
US20050289140A1 true US20050289140A1 (en) | 2005-12-29 |
Family
ID=28794030
Family Applications (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US10/404,992 Expired - Lifetime US6963867B2 (en) | 1999-12-08 | 2003-03-31 | Search query processing to provide category-ranked presentation of search results |
US11/186,403 Abandoned US20050289140A1 (en) | 1999-12-08 | 2005-07-21 | Search query processing to provide category-ranked presentation of search results |
Family Applications Before (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US10/404,992 Expired - Lifetime US6963867B2 (en) | 1999-12-08 | 2003-03-31 | Search query processing to provide category-ranked presentation of search results |
Country Status (1)
Country | Link |
---|---|
US (2) | US6963867B2 (en) |
Cited By (86)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20020035573A1 (en) * | 2000-08-01 | 2002-03-21 | Black Peter M. | Metatag-based datamining |
US20040220903A1 (en) * | 2003-04-30 | 2004-11-04 | Emarkmonitor Inc. | Method and system to correlate trademark data to internet domain name data |
US20050015307A1 (en) * | 2003-04-28 | 2005-01-20 | Simpson Todd Garrett | Method and system of providing location sensitive business information to customers |
US20050060312A1 (en) * | 2003-09-16 | 2005-03-17 | Michael Curtiss | Systems and methods for improving the ranking of news articles |
US20050131872A1 (en) * | 2003-12-16 | 2005-06-16 | Microsoft Corporation | Query recognizer |
US7096214B1 (en) | 1999-12-15 | 2006-08-22 | Google Inc. | System and method for supporting editorial opinion in the ranking of search results |
US20070033228A1 (en) * | 2005-08-03 | 2007-02-08 | Ethan Fassett | System and method for dynamically ranking items of audio content |
US20070038620A1 (en) * | 2005-08-10 | 2007-02-15 | Microsoft Corporation | Consumer-focused results ordering |
US20070073658A1 (en) * | 2005-09-29 | 2007-03-29 | Faerber Franz X | Efficient handling of multipart queries against relational data |
US20070078669A1 (en) * | 2005-09-30 | 2007-04-05 | Dave Kushal B | Selecting representative reviews for display |
US20070124289A1 (en) * | 2005-11-30 | 2007-05-31 | Tomasz Imielinski | Remote execution of actions transparent to a user at registered remote entities in real-time |
US20070156642A1 (en) * | 2005-12-29 | 2007-07-05 | Stoychev Mladen L | Database access method |
US20070174266A1 (en) * | 2006-01-25 | 2007-07-26 | Gu Ta Internet Information Co., Ltd. | Method of optimization of listed result of internet-based search and system based on the method |
US20070174255A1 (en) * | 2005-12-22 | 2007-07-26 | Entrieva, Inc. | Analyzing content to determine context and serving relevant content based on the context |
US20070239701A1 (en) * | 2006-03-29 | 2007-10-11 | International Business Machines Corporation | System and method for prioritizing websites during a webcrawling process |
US20070280537A1 (en) * | 2006-06-05 | 2007-12-06 | Microsoft Corporation | Balancing out-of-dictionary and in-dictionary recognition scores |
US20070288498A1 (en) * | 2006-06-07 | 2007-12-13 | Microsoft Corporation | Interface for managing search term importance relationships |
US20080027979A1 (en) * | 2006-07-31 | 2008-01-31 | Microsoft Corporation | Presenting information related to topics extracted from event classes |
US20080027921A1 (en) * | 2006-07-31 | 2008-01-31 | Microsoft Corporation | Temporal ranking of search results |
US20080028036A1 (en) * | 2006-07-31 | 2008-01-31 | Microsoft Corporation | Adaptive dissemination of personalized and contextually relevant information |
US20080051048A1 (en) * | 2006-08-28 | 2008-02-28 | Assimakis Tzamaloukas | System and method for updating information using limited bandwidth |
US20080052276A1 (en) * | 2006-08-28 | 2008-02-28 | Assimakis Tzamaloukas | System and method for location-based searches and advertising |
US20080059283A1 (en) * | 2006-08-31 | 2008-03-06 | Ebay Inc. | Method and system for opportunity distribution |
US20080059424A1 (en) * | 2006-08-28 | 2008-03-06 | Assimakis Tzamaloukas | System and method for locating-based searches and advertising |
US7346605B1 (en) * | 1999-07-22 | 2008-03-18 | Markmonitor, Inc. | Method and system for searching and monitoring internet trademark usage |
US20080082528A1 (en) * | 2006-10-03 | 2008-04-03 | Pointer S.R.L. | Systems and methods for ranking search engine results |
US20080104042A1 (en) * | 2006-10-25 | 2008-05-01 | Microsoft Corporation | Personalized Search Using Macros |
US20080104113A1 (en) * | 2006-10-26 | 2008-05-01 | Microsoft Corporation | Uniform resource locator scoring for targeted web crawling |
US20080147710A1 (en) * | 2006-12-19 | 2008-06-19 | Microsoft Corporation | Generating web pages utilizing user-defined search parameters |
US20080154880A1 (en) * | 2006-12-26 | 2008-06-26 | Gu Ta Internet Information Co., Ltd. | Method of displaying listed result of internet-based search |
US20080189263A1 (en) * | 2007-02-01 | 2008-08-07 | John Nagle | System and method for improving integrity of internet search |
US20080228675A1 (en) * | 2006-10-13 | 2008-09-18 | Move, Inc. | Multi-tiered cascading crawling system |
US20080249798A1 (en) * | 2007-04-04 | 2008-10-09 | Atul Tulshibagwale | Method and System of Ranking Web Content |
US20080281809A1 (en) * | 2007-05-10 | 2008-11-13 | Microsoft Corporation | Automated analysis of user search behavior |
US20080281808A1 (en) * | 2007-05-10 | 2008-11-13 | Microsoft Corporation | Recommendation of related electronic assets based on user search behavior |
US20080306830A1 (en) * | 2007-06-07 | 2008-12-11 | Cliquality, Llc | System for rating quality of online visitors |
US20080319975A1 (en) * | 2007-06-22 | 2008-12-25 | Microsoft Corporation | Exploratory Search Technique |
US20090006358A1 (en) * | 2007-06-27 | 2009-01-01 | Microsoft Corporation | Search results |
US7493403B2 (en) | 2006-03-13 | 2009-02-17 | Markmonitor Inc. | Domain name ownership validation |
US20090138435A1 (en) * | 2007-11-26 | 2009-05-28 | Leslie Mannion | Techniques for searching and presenting search results |
US20090144266A1 (en) * | 2007-12-04 | 2009-06-04 | Eclipsys Corporation | Search method for entries in a database |
US20090187571A1 (en) * | 2008-01-18 | 2009-07-23 | Treece Jeffrey C | Method Of Putting Items Into Categories According To Rank |
US7568148B1 (en) | 2002-09-20 | 2009-07-28 | Google Inc. | Methods and apparatus for clustering news content |
US20100005446A1 (en) * | 2004-03-31 | 2010-01-07 | Youssef Drissi | Method, system and program product for detecting deviation from software development best practice resource in a code sharing system |
US20100036830A1 (en) * | 2008-08-07 | 2010-02-11 | Yahoo! Inc. | Context based search arrangement for mobile devices |
US7774335B1 (en) * | 2005-08-23 | 2010-08-10 | Amazon Technologies, Inc. | Method and system for determining interest levels of online content navigation paths |
US20100235389A1 (en) * | 2009-03-16 | 2010-09-16 | Yahoo! Inc. | Context based networking |
US7809714B1 (en) | 2007-04-30 | 2010-10-05 | Lawrence Richard Smith | Process for enhancing queries for information retrieval |
US20100293036A1 (en) * | 2009-05-15 | 2010-11-18 | France Telecom | Device and a method for updating a user profile |
US20100318425A1 (en) * | 2009-06-12 | 2010-12-16 | Meherzad Ratan Karanjia | System and method for providing a personalized shopping assistant for online computer users |
US20110093488A1 (en) * | 2009-10-15 | 2011-04-21 | Amacker Matthew W | Dynamic search suggestion and category specific completion |
US20110125736A1 (en) * | 2005-09-30 | 2011-05-26 | Dave Kushal B | Selecting High Quality Reviews for Display |
US20110219441A1 (en) * | 2007-12-07 | 2011-09-08 | Google Inc. | Contextual Query Revision |
WO2011137125A1 (en) * | 2010-04-30 | 2011-11-03 | Alibaba Group Holding Limited | Vertical search-based query method, system and apparatus |
US8090717B1 (en) | 2002-09-20 | 2012-01-03 | Google Inc. | Methods and apparatus for ranking documents |
US20120290568A1 (en) * | 2009-12-29 | 2012-11-15 | Nhn Corporation | System and method for providing search results |
US8335719B1 (en) * | 2007-06-26 | 2012-12-18 | Amazon Technologies, Inc. | Generating advertisement sets based on keywords extracted from data feeds |
US8359309B1 (en) | 2007-05-23 | 2013-01-22 | Google Inc. | Modifying search result ranking based on corpus search statistics |
US8370342B1 (en) * | 2005-09-27 | 2013-02-05 | Google Inc. | Display of relevant results |
US20130080460A1 (en) * | 2011-09-22 | 2013-03-28 | Microsoft Corporation | Providing topic based search guidance |
US20130173783A1 (en) * | 2011-12-30 | 2013-07-04 | Brightedge Technologies, Inc. | System and method for estimating organic web traffic from a secured source |
US20130179420A1 (en) * | 2012-01-09 | 2013-07-11 | Brightedge Technologies, Inc. | Search engine optimization for category specific search results |
WO2012078481A3 (en) * | 2010-12-07 | 2013-08-22 | Alibaba Group Holding Limited | Ranking product information |
US20130339003A1 (en) * | 2012-06-19 | 2013-12-19 | International Business Machines Corporation | Assisted Free Form Decision Definition Using Rules Vocabulary |
US20140052735A1 (en) * | 2006-03-31 | 2014-02-20 | Daniel Egnor | Propagating Information Among Web Pages |
US8719255B1 (en) | 2005-08-23 | 2014-05-06 | Amazon Technologies, Inc. | Method and system for determining interest levels of online content based on rates of change of content access |
US8768907B2 (en) | 2012-04-05 | 2014-07-01 | Brightedge Technologies, Inc. | Ranking search engine results |
US8954463B2 (en) * | 2012-02-29 | 2015-02-10 | International Business Machines Corporation | Use of statistical language modeling for generating exploratory search results |
US8972844B2 (en) * | 2011-10-28 | 2015-03-03 | International Business Machines Corporation | Dynamic digital document fields behavior |
US20150095291A1 (en) * | 2013-09-30 | 2015-04-02 | Wal-Mart Stores, Inc. | Identifying Product Groups in Ecommerce |
US20150294004A1 (en) * | 2010-10-26 | 2015-10-15 | Google Inc. | Rich results relevant to user search queries for books |
US9208437B2 (en) | 2011-12-16 | 2015-12-08 | Alibaba Group Holding Limited | Personalized information pushing method and device |
US9336211B1 (en) | 2013-03-13 | 2016-05-10 | Google Inc. | Associating an entity with a search query |
US9400995B2 (en) | 2011-08-16 | 2016-07-26 | Alibaba Group Holding Limited | Recommending content information based on user behavior |
US9436764B2 (en) | 2010-06-29 | 2016-09-06 | Microsoft Technology Licensing, Llc | Navigation to popular search results |
US9886694B2 (en) | 2013-09-27 | 2018-02-06 | Brightedge Technologies, Inc. | Secured search |
US20180067944A1 (en) * | 2016-09-07 | 2018-03-08 | IntelligenceNODE Consulting Private Limited | Methods and systems for similarity matching |
US10261938B1 (en) | 2012-08-31 | 2019-04-16 | Amazon Technologies, Inc. | Content preloading using predictive models |
US10296659B2 (en) * | 2016-09-26 | 2019-05-21 | International Business Machines Corporation | Search query intent |
RU2698405C2 (en) * | 2013-05-31 | 2019-08-26 | Ксилопикс | Method of search in database |
US20190287145A1 (en) * | 2018-03-19 | 2019-09-19 | Walmart Apollo, Llc | Rule-based automated retail product line linkage |
US10430853B2 (en) | 2004-12-23 | 2019-10-01 | Ebay Inc. | Multiple format search result sets |
CN110659388A (en) * | 2019-10-10 | 2020-01-07 | 北京奇艺世纪科技有限公司 | To-be-recommended information screening method and device, electronic equipment and storage medium |
US10635678B2 (en) | 2014-12-23 | 2020-04-28 | Alibaba Group Holding Limited | Method and apparatus for processing search data |
US10884580B2 (en) * | 2015-06-07 | 2021-01-05 | Apple Inc. | Devices and methods for displaying content in a note-taking application |
US11200498B2 (en) * | 2016-05-12 | 2021-12-14 | International Business Machines Corporation | Altering input search terms |
Families Citing this family (548)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6859212B2 (en) * | 1998-12-08 | 2005-02-22 | Yodlee.Com, Inc. | Interactive transaction center interface |
US6963867B2 (en) * | 1999-12-08 | 2005-11-08 | A9.Com, Inc. | Search query processing to provide category-ranked presentation of search results |
US6757682B1 (en) | 2000-01-28 | 2004-06-29 | Interval Research Corporation | Alerting users to items of current interest |
US8775197B2 (en) * | 2000-02-24 | 2014-07-08 | Webmd, Llc | Personalized health history system with accommodation for consumer health terminology |
US20040230461A1 (en) * | 2000-03-30 | 2004-11-18 | Talib Iqbal A. | Methods and systems for enabling efficient retrieval of data from data collections |
US7260564B1 (en) * | 2000-04-07 | 2007-08-21 | Virage, Inc. | Network video guide and spidering |
US8171509B1 (en) | 2000-04-07 | 2012-05-01 | Virage, Inc. | System and method for applying a database to video multimedia |
US7962948B1 (en) | 2000-04-07 | 2011-06-14 | Virage, Inc. | Video-enabled community building |
US8086697B2 (en) | 2005-06-28 | 2011-12-27 | Claria Innovations, Llc | Techniques for displaying impressions in documents delivered over a computer network |
US7475404B2 (en) | 2000-05-18 | 2009-01-06 | Maquis Techtrix Llc | System and method for implementing click-through for browser executed software including ad proxy and proxy cookie caching |
US7712024B2 (en) | 2000-06-06 | 2010-05-04 | Microsoft Corporation | Application program interfaces for semantically labeling strings and providing actions based on semantically labeled strings |
US7770102B1 (en) | 2000-06-06 | 2010-08-03 | Microsoft Corporation | Method and system for semantically labeling strings and providing actions based on semantically labeled strings |
US7716163B2 (en) * | 2000-06-06 | 2010-05-11 | Microsoft Corporation | Method and system for defining semantic categories and actions |
US7788602B2 (en) | 2000-06-06 | 2010-08-31 | Microsoft Corporation | Method and system for providing restricted actions for recognized semantic categories |
US7490092B2 (en) | 2000-07-06 | 2009-02-10 | Streamsage, Inc. | Method and system for indexing and searching timed media information based upon relevance intervals |
US6999941B1 (en) * | 2000-07-11 | 2006-02-14 | Amazon.Com, Inc. | Providing gift clustering functionality to assist a user in ordering multiple items for a recipient |
IL140241A (en) * | 2000-12-11 | 2007-02-11 | Celebros Ltd | Interactive searching system and method |
US7299255B2 (en) | 2000-09-26 | 2007-11-20 | I2 Technologies Us, Inc. | System and method for migrating data in an electronic commerce system |
US7590558B2 (en) * | 2000-09-26 | 2009-09-15 | I2 Technologies Us, Inc. | System and method for facilitating electronic commerce transactions |
US20020111870A1 (en) * | 2000-09-26 | 2002-08-15 | I2 Technologies, Inc. | System and method for identifying a product |
AU2001296537A1 (en) * | 2000-10-02 | 2002-04-15 | Enic Corporation | Determining alternative textual identifiers, such as for registered domain names |
JP2002149699A (en) * | 2000-11-10 | 2002-05-24 | Hitachi Ltd | Data retrieving device |
US7016892B1 (en) * | 2000-11-17 | 2006-03-21 | Cnet Networks, Inc. | Apparatus and method for delivering information over a network |
US8402068B2 (en) | 2000-12-07 | 2013-03-19 | Half.Com, Inc. | System and method for collecting, associating, normalizing and presenting product and vendor information on a distributed network |
US7275102B2 (en) * | 2001-01-22 | 2007-09-25 | Sun Microsystems, Inc. | Trust mechanisms for a peer-to-peer network computing platform |
US7089237B2 (en) * | 2001-01-26 | 2006-08-08 | Google, Inc. | Interface and system for providing persistent contextual relevance for commerce activities in a networked environment |
US7047482B1 (en) * | 2001-02-28 | 2006-05-16 | Gary Odom | Automatic directory supplementation |
US7778816B2 (en) | 2001-04-24 | 2010-08-17 | Microsoft Corporation | Method and system for applying input mode bias |
US6983276B2 (en) * | 2001-05-15 | 2006-01-03 | I2 Technologies Us, Inc. | Facilitating electronic commerce transactions using buyer profiles |
US7349868B2 (en) * | 2001-05-15 | 2008-03-25 | I2 Technologies Us, Inc. | Pre-qualifying sellers during the matching phase of an electronic commerce transaction |
US7475030B1 (en) | 2001-05-16 | 2009-01-06 | I2 Technologies Us, Inc. | Facilitating electronic commerce transactions using a shared product data repository |
US7263515B1 (en) | 2001-06-18 | 2007-08-28 | I2 Technologies Us, Inc. | Content enhancement in an electronic marketplace |
US7127416B1 (en) | 2001-06-18 | 2006-10-24 | I2 Technologies Us, Inc. | Distributed processing of sorted search results in an electronic commerce system and method |
US7330829B1 (en) | 2001-06-26 | 2008-02-12 | I2 Technologies Us, Inc. | Providing market feedback associated with electronic commerce transactions to sellers |
US7809672B1 (en) | 2001-06-28 | 2010-10-05 | I2 Technologies Us, Inc. | Association of data with a product classification schema |
US8086643B1 (en) | 2001-06-28 | 2011-12-27 | Jda Software Group, Inc. | Translation between product classification schemas |
US7346560B1 (en) | 2001-06-29 | 2008-03-18 | I2 Technologies Us, Inc. | Protecting content from unauthorized reproduction |
US7162453B1 (en) | 2001-06-29 | 2007-01-09 | I2 Technologies Us, Inc. | Protecting content from unauthorized reproduction |
EP1276061A1 (en) * | 2001-07-09 | 2003-01-15 | Accenture | Computer based system and method of determining a satisfaction index of a text |
US7720842B2 (en) * | 2001-07-16 | 2010-05-18 | Informatica Corporation | Value-chained queries in analytic applications |
US7308496B2 (en) * | 2001-07-31 | 2007-12-11 | Sun Microsystems, Inc. | Representing trust in distributed peer-to-peer networks |
US7203753B2 (en) * | 2001-07-31 | 2007-04-10 | Sun Microsystems, Inc. | Propagating and updating trust relationships in distributed peer-to-peer networks |
US7383433B2 (en) | 2001-07-31 | 2008-06-03 | Sun Microsystems, Inc. | Trust spectrum for certificate distribution in distributed peer-to-peer networks |
US7222187B2 (en) * | 2001-07-31 | 2007-05-22 | Sun Microsystems, Inc. | Distributed trust mechanism for decentralized networks |
JP4423584B2 (en) * | 2001-09-04 | 2010-03-03 | ヤマハ株式会社 | Electronic music equipment |
JP2003091552A (en) * | 2001-09-17 | 2003-03-28 | Hitachi Ltd | Retrieval requested information extraction method, its operating system and processing program of the same |
US20030061232A1 (en) * | 2001-09-21 | 2003-03-27 | Dun & Bradstreet Inc. | Method and system for processing business data |
US7818243B1 (en) * | 2001-10-12 | 2010-10-19 | I2 Technologies Us, Inc. | Displaying strikes between bids and asks in a market over time using polygons |
US6763362B2 (en) * | 2001-11-30 | 2004-07-13 | Micron Technology, Inc. | Method and system for updating a search engine |
US8261306B2 (en) * | 2001-12-11 | 2012-09-04 | Koninklijke Philips Electronics N.V. | System for and method of shopping through television |
US7197493B2 (en) * | 2001-12-21 | 2007-03-27 | Lifestory Productions, Inc. | Collection management database of arbitrary schema |
US7565367B2 (en) * | 2002-01-15 | 2009-07-21 | Iac Search & Media, Inc. | Enhanced popularity ranking |
US20080027769A1 (en) | 2002-09-09 | 2008-01-31 | Jeff Scott Eder | Knowledge based performance management system |
US7127613B2 (en) * | 2002-02-25 | 2006-10-24 | Sun Microsystems, Inc. | Secured peer-to-peer network data exchange |
US8590013B2 (en) | 2002-02-25 | 2013-11-19 | C. S. Lee Crawford | Method of managing and communicating data pertaining to software applications for processor-based devices comprising wireless communication circuitry |
US7949648B2 (en) * | 2002-02-26 | 2011-05-24 | Soren Alain Mortensen | Compiling and accessing subject-specific information from a computer network |
CN1445696A (en) * | 2002-03-18 | 2003-10-01 | 朗迅科技公司 | Method for automatic searching similar image in image data base |
US7412424B1 (en) | 2002-03-19 | 2008-08-12 | I2 Technologies Us, Inc. | Third party certification of content in electronic commerce transactions |
US7512649B2 (en) * | 2002-03-22 | 2009-03-31 | Sun Microsytems, Inc. | Distributed identities |
US20030225763A1 (en) * | 2002-04-15 | 2003-12-04 | Microsoft Corporation | Self-improving system and method for classifying pages on the world wide web |
DE10319277A1 (en) * | 2002-05-02 | 2003-11-13 | I2 Technologies Inc | Sales offer display system for e-commerce, has inter-linked markers in offer variable windows, that move relatively to indicate specific offer price in different offer windows |
US20050108195A1 (en) * | 2002-05-07 | 2005-05-19 | Microsoft Corporation | Method, system, and apparatus for processing information based on the discovery of semantically labeled strings |
US7707496B1 (en) | 2002-05-09 | 2010-04-27 | Microsoft Corporation | Method, system, and apparatus for converting dates between calendars and languages based upon semantically labeled strings |
US7742048B1 (en) | 2002-05-23 | 2010-06-22 | Microsoft Corporation | Method, system, and apparatus for converting numbers based upon semantically labeled strings |
US7707024B2 (en) | 2002-05-23 | 2010-04-27 | Microsoft Corporation | Method, system, and apparatus for converting currency values based upon semantically labeled strings |
US8260786B2 (en) * | 2002-05-24 | 2012-09-04 | Yahoo! Inc. | Method and apparatus for categorizing and presenting documents of a distributed database |
US7827546B1 (en) | 2002-06-05 | 2010-11-02 | Microsoft Corporation | Mechanism for downloading software components from a remote source for use by a local software application |
US7356537B2 (en) | 2002-06-06 | 2008-04-08 | Microsoft Corporation | Providing contextually sensitive tools and help content in computer-generated documents |
US7716676B2 (en) | 2002-06-25 | 2010-05-11 | Microsoft Corporation | System and method for issuing a message to a program |
US7209915B1 (en) | 2002-06-28 | 2007-04-24 | Microsoft Corporation | Method, system and apparatus for routing a query to one or more providers |
US7058638B2 (en) * | 2002-09-03 | 2006-06-06 | Research Triangle Institute | Method for statistical disclosure limitation |
KR100476933B1 (en) * | 2002-10-10 | 2005-03-16 | 삼성전자주식회사 | Semiconductor Wafer Having Identification Indication |
US20050125240A9 (en) * | 2002-10-21 | 2005-06-09 | Speiser Leonard R. | Product recommendation in a network-based commerce system |
US8108455B2 (en) * | 2002-10-31 | 2012-01-31 | Oracle America, Inc. | Mobile agents in peer-to-peer networks |
US7254608B2 (en) * | 2002-10-31 | 2007-08-07 | Sun Microsystems, Inc. | Managing distribution of content using mobile agents in peer-topeer networks |
US8037202B2 (en) * | 2002-10-31 | 2011-10-11 | Oracle America, Inc. | Presence detection using mobile agents in peer-to-peer networks |
US7328243B2 (en) * | 2002-10-31 | 2008-02-05 | Sun Microsystems, Inc. | Collaborative content coherence using mobile agents in peer-to-peer networks |
US7213047B2 (en) * | 2002-10-31 | 2007-05-01 | Sun Microsystems, Inc. | Peer trust evaluation using mobile agents in peer-to-peer networks |
US7603341B2 (en) | 2002-11-05 | 2009-10-13 | Claria Corporation | Updating the content of a presentation vehicle in a computer network |
US20040098380A1 (en) * | 2002-11-19 | 2004-05-20 | Dentel Stephen D. | Method, system and apparatus for providing a search system |
US8195631B2 (en) * | 2002-12-23 | 2012-06-05 | Sap Ag | Resource finder tool |
US8819039B2 (en) | 2002-12-31 | 2014-08-26 | Ebay Inc. | Method and system to generate a listing in a network-based commerce system |
US20050055353A1 (en) * | 2003-01-30 | 2005-03-10 | Marx Peter Samuel | Acquiring, managing, distributing, and presenting contextual data relating to a known postion for location-based services to and between users |
US7783614B2 (en) | 2003-02-13 | 2010-08-24 | Microsoft Corporation | Linking elements of a document to corresponding fields, queries and/or procedures in a database |
US7783617B2 (en) * | 2003-04-16 | 2010-08-24 | Yahoo! Inc. | Personals advertisement affinities in a networked computer system |
US7711550B1 (en) | 2003-04-29 | 2010-05-04 | Microsoft Corporation | Methods and system for recognizing names in a computer-generated document and for providing helpful actions associated with recognized names |
US7558841B2 (en) * | 2003-05-14 | 2009-07-07 | Microsoft Corporation | Method, system, and computer-readable medium for communicating results to a data query in a computer network |
US7401072B2 (en) * | 2003-06-10 | 2008-07-15 | Google Inc. | Named URL entry |
US7739295B1 (en) | 2003-06-20 | 2010-06-15 | Amazon Technologies, Inc. | Method and system for identifying information relevant to content |
US7739588B2 (en) | 2003-06-27 | 2010-06-15 | Microsoft Corporation | Leveraging markup language data for semantically labeling text strings and data and for providing actions based on semantically labeled text strings and data |
US7428700B2 (en) * | 2003-07-28 | 2008-09-23 | Microsoft Corporation | Vision-based document segmentation |
US7505964B2 (en) | 2003-09-12 | 2009-03-17 | Google Inc. | Methods and systems for improving a search ranking using related queries |
US7739281B2 (en) * | 2003-09-16 | 2010-06-15 | Microsoft Corporation | Systems and methods for ranking documents based upon structurally interrelated information |
US7130819B2 (en) * | 2003-09-30 | 2006-10-31 | Yahoo! Inc. | Method and computer readable medium for search scoring |
US7447688B2 (en) * | 2003-10-09 | 2008-11-04 | International Business Machines Corporation | System for conducting searches on the world wide web enabling the search requester to modify the domain context of a search responsive to an excessive number of hits on combinations of keywords |
US7836051B1 (en) * | 2003-10-13 | 2010-11-16 | Amazon Technologies, Inc. | Predictive analysis of browse activity data of users of a database access system in which items are arranged in a hierarchy |
US7165119B2 (en) * | 2003-10-14 | 2007-01-16 | America Online, Inc. | Search enhancement system and method having rankings, explicitly specified by the user, based upon applicability and validity of search parameters in regard to a subject matter |
US7640232B2 (en) * | 2003-10-14 | 2009-12-29 | Aol Llc | Search enhancement system with information from a selected source |
US20060235873A1 (en) * | 2003-10-22 | 2006-10-19 | Jookster Networks, Inc. | Social network-based internet search engine |
US20050091202A1 (en) * | 2003-10-22 | 2005-04-28 | Thomas Kapenda J. | Social network-based internet search engine |
US20050097089A1 (en) * | 2003-11-05 | 2005-05-05 | Tom Nielsen | Persistent user interface for providing navigational functionality |
US7231399B1 (en) | 2003-11-14 | 2007-06-12 | Google Inc. | Ranking documents based on large data sets |
CA2546514C (en) * | 2003-11-17 | 2013-04-30 | Bloomberg Lp | Legal research system |
US7844589B2 (en) * | 2003-11-18 | 2010-11-30 | Yahoo! Inc. | Method and apparatus for performing a search |
US8170912B2 (en) | 2003-11-25 | 2012-05-01 | Carhamm Ltd., Llc | Database structure and front end |
US7523096B2 (en) | 2003-12-03 | 2009-04-21 | Google Inc. | Methods and systems for personalized network searching |
US7451131B2 (en) * | 2003-12-08 | 2008-11-11 | Iac Search & Media, Inc. | Methods and systems for providing a response to a query |
US20060230040A1 (en) * | 2003-12-08 | 2006-10-12 | Andy Curtis | Methods and systems for providing a response to a query |
US7181447B2 (en) * | 2003-12-08 | 2007-02-20 | Iac Search And Media, Inc. | Methods and systems for conceptually organizing and presenting information |
KR100452086B1 (en) * | 2003-12-22 | 2004-10-13 | 엔에이치엔(주) | Search System For Providing Information of Keyword Input Frequency By Category And Method Thereof |
US7243099B2 (en) * | 2003-12-23 | 2007-07-10 | Proclarity Corporation | Computer-implemented method, system, apparatus for generating user's insight selection by showing an indication of popularity, displaying one or more materialized insight associated with specified item class within the database that potentially match the search |
US7447678B2 (en) * | 2003-12-31 | 2008-11-04 | Google Inc. | Interface for a universal search engine |
US8375048B1 (en) * | 2004-01-20 | 2013-02-12 | Microsoft Corporation | Query augmentation |
US8655904B2 (en) * | 2004-02-11 | 2014-02-18 | Ebay, Inc. | Method and system to enhance data integrity in a database |
US7814085B1 (en) * | 2004-02-26 | 2010-10-12 | Google Inc. | System and method for determining a composite score for categorized search results |
US7831581B1 (en) | 2004-03-01 | 2010-11-09 | Radix Holdings, Llc | Enhanced search |
US8676830B2 (en) * | 2004-03-04 | 2014-03-18 | Yahoo! Inc. | Keyword recommendation for internet search engines |
US7689543B2 (en) * | 2004-03-11 | 2010-03-30 | International Business Machines Corporation | Search engine providing match and alternative answers using cumulative probability values |
US7246117B2 (en) * | 2004-03-31 | 2007-07-17 | Sap Ag | Algorithm for fast disk based text mining |
US8914383B1 (en) | 2004-04-06 | 2014-12-16 | Monster Worldwide, Inc. | System and method for providing job recommendations |
US7519581B2 (en) * | 2004-04-30 | 2009-04-14 | Yahoo! Inc. | Method and apparatus for performing a search |
US8090698B2 (en) * | 2004-05-07 | 2012-01-03 | Ebay Inc. | Method and system to facilitate a search of an information resource |
WO2005114379A2 (en) * | 2004-05-14 | 2005-12-01 | Perfect Market Technologies, Inc. | Personalized search engine |
US7739142B2 (en) * | 2004-05-17 | 2010-06-15 | Yahoo! Inc. | System and method for providing automobile marketing research information |
US20050283464A1 (en) * | 2004-06-10 | 2005-12-22 | Allsup James F | Method and apparatus for selective internet advertisement |
US7716225B1 (en) | 2004-06-17 | 2010-05-11 | Google Inc. | Ranking documents based on user behavior and/or feature data |
US7720862B2 (en) * | 2004-06-22 | 2010-05-18 | Sap Ag | Request-based knowledge acquisition |
US7437358B2 (en) | 2004-06-25 | 2008-10-14 | Apple Inc. | Methods and systems for managing data |
US7774326B2 (en) | 2004-06-25 | 2010-08-10 | Apple Inc. | Methods and systems for managing data |
US7730012B2 (en) | 2004-06-25 | 2010-06-01 | Apple Inc. | Methods and systems for managing data |
US7428530B2 (en) * | 2004-07-01 | 2008-09-23 | Microsoft Corporation | Dispersing search engine results by using page category information |
US7933818B1 (en) | 2004-07-13 | 2011-04-26 | Amazon Technologies, Inc. | Service for automatically detecting and responding to transition events that occur during browsing of an electronic catalog |
US7519595B2 (en) * | 2004-07-14 | 2009-04-14 | Microsoft Corporation | Method and system for adaptive categorial presentation of search results |
US7533074B2 (en) * | 2004-07-23 | 2009-05-12 | Sap Ag | Modifiable knowledge base in a mobile device |
US7536408B2 (en) | 2004-07-26 | 2009-05-19 | Google Inc. | Phrase-based indexing in an information retrieval system |
US7580929B2 (en) * | 2004-07-26 | 2009-08-25 | Google Inc. | Phrase-based personalization of searches in an information retrieval system |
US7567959B2 (en) | 2004-07-26 | 2009-07-28 | Google Inc. | Multiple index based information retrieval system |
US7711679B2 (en) | 2004-07-26 | 2010-05-04 | Google Inc. | Phrase-based detection of duplicate documents in an information retrieval system |
US7702618B1 (en) | 2004-07-26 | 2010-04-20 | Google Inc. | Information retrieval system for archiving multiple document versions |
US7580921B2 (en) | 2004-07-26 | 2009-08-25 | Google Inc. | Phrase identification in an information retrieval system |
US7599914B2 (en) | 2004-07-26 | 2009-10-06 | Google Inc. | Phrase-based searching in an information retrieval system |
US7584175B2 (en) | 2004-07-26 | 2009-09-01 | Google Inc. | Phrase-based generation of document descriptions |
US7199571B2 (en) * | 2004-07-27 | 2007-04-03 | Optisense Network, Inc. | Probe apparatus for use in a separable connector, and systems including same |
US8078602B2 (en) | 2004-12-17 | 2011-12-13 | Claria Innovations, Llc | Search engine for a computer network |
JP2008511057A (en) * | 2004-08-19 | 2008-04-10 | クラリア コーポレイション | Method and apparatus for responding to end-user information requests |
US8255413B2 (en) * | 2004-08-19 | 2012-08-28 | Carhamm Ltd., Llc | Method and apparatus for responding to request for information-personalization |
US10959090B1 (en) | 2004-08-25 | 2021-03-23 | Richard Paiz | Personal search results |
US11468128B1 (en) | 2006-10-20 | 2022-10-11 | Richard Paiz | Search engine optimizer |
CN101014954B (en) * | 2004-09-07 | 2010-05-05 | 因特曼股份有限公司 | Information search provision apparatus and information search provision system |
US7321889B2 (en) * | 2004-09-10 | 2008-01-22 | Suggestica, Inc. | Authoring and managing personalized searchable link collections |
US7490082B2 (en) * | 2004-09-16 | 2009-02-10 | International Business Machines Corporation | System and method for searching internet domains |
US8577865B2 (en) * | 2004-09-29 | 2013-11-05 | Sap Ag | Document searching system |
US20060075327A1 (en) * | 2004-09-29 | 2006-04-06 | Joe Sriver | User interface for presentation of a document |
US8364668B2 (en) | 2004-09-29 | 2013-01-29 | Google Inc. | User interfaces for a document search engine |
US8595225B1 (en) * | 2004-09-30 | 2013-11-26 | Google Inc. | Systems and methods for correlating document topicality and popularity |
US20060074883A1 (en) * | 2004-10-05 | 2006-04-06 | Microsoft Corporation | Systems, methods, and interfaces for providing personalized search and information access |
WO2006047790A2 (en) * | 2004-10-27 | 2006-05-04 | Client Dynamics, Inc. | Enhanced client relationship management systems and methods with a recommendation engine |
US20060112078A1 (en) * | 2004-11-22 | 2006-05-25 | Bellsouth Intellectual Property Corporation | Information procurement |
US7693863B2 (en) | 2004-12-20 | 2010-04-06 | Claria Corporation | Method and device for publishing cross-network user behavioral data |
US7769579B2 (en) | 2005-05-31 | 2010-08-03 | Google Inc. | Learning facts from semi-structured text |
US8244689B2 (en) | 2006-02-17 | 2012-08-14 | Google Inc. | Attribute entropy as a signal in object normalization |
US7974962B2 (en) * | 2005-01-06 | 2011-07-05 | Aptiv Digital, Inc. | Search engine for a video recorder |
US7703030B2 (en) | 2005-01-11 | 2010-04-20 | Trusted Opinion, Inc. | Method and system for providing customized recommendations to users |
US20060161520A1 (en) * | 2005-01-14 | 2006-07-20 | Microsoft Corporation | System and method for generating alternative search terms |
US20060162546A1 (en) * | 2005-01-21 | 2006-07-27 | Sanden Corporation | Sealing member of a compressor |
US7624102B2 (en) * | 2005-01-28 | 2009-11-24 | Microsoft Corporation | System and method for grouping by attribute |
US7689556B2 (en) * | 2005-01-31 | 2010-03-30 | France Telecom | Content navigation service |
US20060173822A1 (en) * | 2005-02-03 | 2006-08-03 | Microsoft Corporation | System and method for optimization of results based on monetization intent |
US20060179069A1 (en) * | 2005-02-04 | 2006-08-10 | Bechtel Michael E | Knowledge discovery tool navigation |
US20060179026A1 (en) * | 2005-02-04 | 2006-08-10 | Bechtel Michael E | Knowledge discovery tool extraction and integration |
US7792811B2 (en) * | 2005-02-16 | 2010-09-07 | Transaxtions Llc | Intelligent search with guiding info |
US8131736B1 (en) | 2005-03-01 | 2012-03-06 | Google Inc. | System and method for navigating documents |
US8645941B2 (en) | 2005-03-07 | 2014-02-04 | Carhamm Ltd., Llc | Method for attributing and allocating revenue related to embedded software |
WO2006099300A2 (en) * | 2005-03-11 | 2006-09-21 | Yahoo!Inc. | System and method for listing data acquisition |
US20060206517A1 (en) * | 2005-03-11 | 2006-09-14 | Yahoo! Inc. | System and method for listing administration |
US8019749B2 (en) * | 2005-03-17 | 2011-09-13 | Roy Leban | System, method, and user interface for organizing and searching information |
US8073866B2 (en) | 2005-03-17 | 2011-12-06 | Claria Innovations, Llc | Method for providing content to an internet user based on the user's demonstrated content preferences |
US20060224571A1 (en) * | 2005-03-30 | 2006-10-05 | Jean-Michel Leon | Methods and systems to facilitate searching a data resource |
US9208229B2 (en) | 2005-03-31 | 2015-12-08 | Google Inc. | Anchor text summarization for corroboration |
US8423541B1 (en) | 2005-03-31 | 2013-04-16 | Google Inc. | Using saved search results for quality feedback |
US8682913B1 (en) | 2005-03-31 | 2014-03-25 | Google Inc. | Corroborating facts extracted from multiple sources |
US7587387B2 (en) | 2005-03-31 | 2009-09-08 | Google Inc. | User interface for facts query engine with snippets from information sources that include query terms and answer terms |
JP2006285855A (en) * | 2005-04-04 | 2006-10-19 | Ntt Docomo Inc | Search server |
WO2006108069A2 (en) * | 2005-04-06 | 2006-10-12 | Google, Inc. | Searching through content which is accessible through web-based forms |
US7401073B2 (en) * | 2005-04-28 | 2008-07-15 | International Business Machines Corporation | Term-statistics modification for category-based search |
US8214264B2 (en) | 2005-05-02 | 2012-07-03 | Cbs Interactive, Inc. | System and method for an electronic product advisor |
US7734644B2 (en) * | 2005-05-06 | 2010-06-08 | Seaton Gras | System and method for hierarchical information retrieval from a coded collection of relational data |
US7546289B2 (en) * | 2005-05-11 | 2009-06-09 | W.W. Grainger, Inc. | System and method for providing a response to a search query |
CN101366024B (en) * | 2005-05-16 | 2014-07-30 | 电子湾有限公司 | Method and system for processing data searching request |
US8527510B2 (en) | 2005-05-23 | 2013-09-03 | Monster Worldwide, Inc. | Intelligent job matching system and method |
US8433713B2 (en) | 2005-05-23 | 2013-04-30 | Monster Worldwide, Inc. | Intelligent job matching system and method |
US8375067B2 (en) | 2005-05-23 | 2013-02-12 | Monster Worldwide, Inc. | Intelligent job matching system and method including negative filtration |
JP4772378B2 (en) * | 2005-05-26 | 2011-09-14 | 株式会社東芝 | Method and apparatus for generating time-series data from a web page |
US7831545B1 (en) | 2005-05-31 | 2010-11-09 | Google Inc. | Identifying the unifying subject of a set of facts |
US8996470B1 (en) | 2005-05-31 | 2015-03-31 | Google Inc. | System for ensuring the internal consistency of a fact repository |
JP4455415B2 (en) * | 2005-06-10 | 2010-04-21 | キヤノン株式会社 | Display method and display control apparatus |
CA2545237A1 (en) | 2005-07-29 | 2007-01-29 | Cognos Incorporated | Method and system for managing exemplar terms database for business-oriented metadata content |
CA2545232A1 (en) * | 2005-07-29 | 2007-01-29 | Cognos Incorporated | Method and system for creating a taxonomy from business-oriented metadata content |
US7516124B2 (en) * | 2005-12-20 | 2009-04-07 | Yahoo! Inc. | Interactive search engine |
US7747639B2 (en) * | 2005-08-24 | 2010-06-29 | Yahoo! Inc. | Alternative search query prediction |
US7672932B2 (en) * | 2005-08-24 | 2010-03-02 | Yahoo! Inc. | Speculative search result based on a not-yet-submitted search query |
US7844599B2 (en) * | 2005-08-24 | 2010-11-30 | Yahoo! Inc. | Biasing queries to determine suggested queries |
US7752209B2 (en) | 2005-09-14 | 2010-07-06 | Jumptap, Inc. | Presenting sponsored content on a mobile communication facility |
US8688671B2 (en) | 2005-09-14 | 2014-04-01 | Millennial Media | Managing sponsored content based on geographic region |
US10038756B2 (en) | 2005-09-14 | 2018-07-31 | Millenial Media LLC | Managing sponsored content based on device characteristics |
US8131271B2 (en) | 2005-11-05 | 2012-03-06 | Jumptap, Inc. | Categorization of a mobile user profile based on browse behavior |
US8311888B2 (en) | 2005-09-14 | 2012-11-13 | Jumptap, Inc. | Revenue models associated with syndication of a behavioral profile using a monetization platform |
US8812526B2 (en) | 2005-09-14 | 2014-08-19 | Millennial Media, Inc. | Mobile content cross-inventory yield optimization |
US8364540B2 (en) | 2005-09-14 | 2013-01-29 | Jumptap, Inc. | Contextual targeting of content using a monetization platform |
US8156128B2 (en) | 2005-09-14 | 2012-04-10 | Jumptap, Inc. | Contextual mobile content placement on a mobile communication facility |
US8229914B2 (en) | 2005-09-14 | 2012-07-24 | Jumptap, Inc. | Mobile content spidering and compatibility determination |
US7660581B2 (en) | 2005-09-14 | 2010-02-09 | Jumptap, Inc. | Managing sponsored content based on usage history |
US7769764B2 (en) | 2005-09-14 | 2010-08-03 | Jumptap, Inc. | Mobile advertisement syndication |
US7702318B2 (en) | 2005-09-14 | 2010-04-20 | Jumptap, Inc. | Presentation of sponsored content based on mobile transaction event |
US9703892B2 (en) | 2005-09-14 | 2017-07-11 | Millennial Media Llc | Predictive text completion for a mobile communication facility |
US8832100B2 (en) | 2005-09-14 | 2014-09-09 | Millennial Media, Inc. | User transaction history influenced search results |
US9201979B2 (en) | 2005-09-14 | 2015-12-01 | Millennial Media, Inc. | Syndication of a behavioral profile associated with an availability condition using a monetization platform |
US8302030B2 (en) | 2005-09-14 | 2012-10-30 | Jumptap, Inc. | Management of multiple advertising inventories using a monetization platform |
US8503995B2 (en) | 2005-09-14 | 2013-08-06 | Jumptap, Inc. | Mobile dynamic advertisement creation and placement |
US7676394B2 (en) | 2005-09-14 | 2010-03-09 | Jumptap, Inc. | Dynamic bidding and expected value |
US8364521B2 (en) | 2005-09-14 | 2013-01-29 | Jumptap, Inc. | Rendering targeted advertisement on mobile communication facilities |
US8805339B2 (en) | 2005-09-14 | 2014-08-12 | Millennial Media, Inc. | Categorization of a mobile user profile based on browse and viewing behavior |
US8290810B2 (en) | 2005-09-14 | 2012-10-16 | Jumptap, Inc. | Realtime surveying within mobile sponsored content |
US7577665B2 (en) | 2005-09-14 | 2009-08-18 | Jumptap, Inc. | User characteristic influenced search results |
US20070198485A1 (en) * | 2005-09-14 | 2007-08-23 | Jorey Ramer | Mobile search service discovery |
US7603360B2 (en) | 2005-09-14 | 2009-10-13 | Jumptap, Inc. | Location influenced search results |
US8238888B2 (en) | 2006-09-13 | 2012-08-07 | Jumptap, Inc. | Methods and systems for mobile coupon placement |
US8615719B2 (en) | 2005-09-14 | 2013-12-24 | Jumptap, Inc. | Managing sponsored content for delivery to mobile communication facilities |
US7912458B2 (en) | 2005-09-14 | 2011-03-22 | Jumptap, Inc. | Interaction analysis and prioritization of mobile content |
US8209344B2 (en) | 2005-09-14 | 2012-06-26 | Jumptap, Inc. | Embedding sponsored content in mobile applications |
US10911894B2 (en) | 2005-09-14 | 2021-02-02 | Verizon Media Inc. | Use of dynamic content generation parameters based on previous performance of those parameters |
US9058406B2 (en) | 2005-09-14 | 2015-06-16 | Millennial Media, Inc. | Management of multiple advertising inventories using a monetization platform |
US20110313853A1 (en) | 2005-09-14 | 2011-12-22 | Jorey Ramer | System for targeting advertising content to a plurality of mobile communication facilities |
US8195133B2 (en) | 2005-09-14 | 2012-06-05 | Jumptap, Inc. | Mobile dynamic advertisement creation and placement |
US10592930B2 (en) | 2005-09-14 | 2020-03-17 | Millenial Media, LLC | Syndication of a behavioral profile using a monetization platform |
US9471925B2 (en) | 2005-09-14 | 2016-10-18 | Millennial Media Llc | Increasing mobile interactivity |
US8660891B2 (en) | 2005-11-01 | 2014-02-25 | Millennial Media | Interactive mobile advertisement banners |
US8989718B2 (en) | 2005-09-14 | 2015-03-24 | Millennial Media, Inc. | Idle screen advertising |
US8819659B2 (en) | 2005-09-14 | 2014-08-26 | Millennial Media, Inc. | Mobile search service instant activation |
US8103545B2 (en) | 2005-09-14 | 2012-01-24 | Jumptap, Inc. | Managing payment for sponsored content presented to mobile communication facilities |
US8027879B2 (en) | 2005-11-05 | 2011-09-27 | Jumptap, Inc. | Exclusivity bidding for mobile sponsored content |
US8532633B2 (en) | 2005-09-14 | 2013-09-10 | Jumptap, Inc. | System for targeting advertising content to a plurality of mobile communication facilities |
US8666376B2 (en) | 2005-09-14 | 2014-03-04 | Millennial Media | Location based mobile shopping affinity program |
US7860871B2 (en) | 2005-09-14 | 2010-12-28 | Jumptap, Inc. | User history influenced search results |
US7548915B2 (en) | 2005-09-14 | 2009-06-16 | Jorey Ramer | Contextual mobile content placement on a mobile communication facility |
US9076175B2 (en) | 2005-09-14 | 2015-07-07 | Millennial Media, Inc. | Mobile comparison shopping |
US7788590B2 (en) * | 2005-09-26 | 2010-08-31 | Microsoft Corporation | Lightweight reference user interface |
US7992085B2 (en) | 2005-09-26 | 2011-08-02 | Microsoft Corporation | Lightweight reference user interface |
US7783632B2 (en) * | 2005-11-03 | 2010-08-24 | Microsoft Corporation | Using popularity data for ranking |
US8175585B2 (en) | 2005-11-05 | 2012-05-08 | Jumptap, Inc. | System for targeting advertising content to a plurality of mobile communication facilities |
US8571999B2 (en) | 2005-11-14 | 2013-10-29 | C. S. Lee Crawford | Method of conducting operations for a social network application including activity list generation |
US20070118441A1 (en) * | 2005-11-22 | 2007-05-24 | Robert Chatwani | Editable electronic catalogs |
US8977603B2 (en) | 2005-11-22 | 2015-03-10 | Ebay Inc. | System and method for managing shared collections |
US8132103B1 (en) * | 2006-07-19 | 2012-03-06 | Aol Inc. | Audio and/or video scene detection and retrieval |
US8751502B2 (en) * | 2005-11-29 | 2014-06-10 | Aol Inc. | Visually-represented results to search queries in rich media content |
US7627559B2 (en) * | 2005-12-15 | 2009-12-01 | Microsoft Corporation | Context-based key phrase discovery and similarity measurement utilizing search engine query logs |
US7925649B2 (en) | 2005-12-30 | 2011-04-12 | Google Inc. | Method, system, and graphical user interface for alerting a computer user to new results for a prior search |
US7457772B2 (en) * | 2006-01-06 | 2008-11-25 | Tabin Joshua Z | Method and apparatus for interactive criteria-based commodity comparisons |
US20080016441A1 (en) * | 2006-01-06 | 2008-01-17 | Tabin Joshua Z | Method and Apparatus to Facilitate Altering a Presentation Order for Search Results |
US8195657B1 (en) | 2006-01-09 | 2012-06-05 | Monster Worldwide, Inc. | Apparatuses, systems and methods for data entry correlation |
US8166412B2 (en) * | 2006-01-13 | 2012-04-24 | Adobe Systems Incorporated | Visual cue discernment on scattered data |
EP1920366A1 (en) * | 2006-01-20 | 2008-05-14 | Glenbrook Associates, Inc. | System and method for context-rich database optimized for processing of concepts |
US7991797B2 (en) | 2006-02-17 | 2011-08-02 | Google Inc. | ID persistence through normalization |
US8260785B2 (en) | 2006-02-17 | 2012-09-04 | Google Inc. | Automatic object reference identification and linking in a browseable fact repository |
US20070192130A1 (en) * | 2006-01-31 | 2007-08-16 | Haramol Singh Sandhu | System and method for rating service providers |
US7640234B2 (en) * | 2006-02-09 | 2009-12-29 | Ebay Inc. | Methods and systems to communicate information |
US7849047B2 (en) * | 2006-02-09 | 2010-12-07 | Ebay Inc. | Method and system to analyze domain rules based on domain coverage of the domain rules |
US7725417B2 (en) * | 2006-02-09 | 2010-05-25 | Ebay Inc. | Method and system to analyze rules based on popular query coverage |
US7739225B2 (en) | 2006-02-09 | 2010-06-15 | Ebay Inc. | Method and system to analyze aspect rules based on domain coverage of an aspect-value pair |
US9443333B2 (en) | 2006-02-09 | 2016-09-13 | Ebay Inc. | Methods and systems to communicate information |
US8862572B2 (en) * | 2006-02-17 | 2014-10-14 | Google Inc. | Sharing user distributed search results |
US7844603B2 (en) * | 2006-02-17 | 2010-11-30 | Google Inc. | Sharing user distributed search results |
US8122019B2 (en) * | 2006-02-17 | 2012-02-21 | Google Inc. | Sharing user distributed search results |
US8700568B2 (en) | 2006-02-17 | 2014-04-15 | Google Inc. | Entity normalization via name normalization |
US20070208784A1 (en) * | 2006-03-03 | 2007-09-06 | Beeston Ralph T | Parsing computer system logging information collected by common logging |
US7676521B2 (en) * | 2006-03-31 | 2010-03-09 | Microsoft Corporation | Keyword search volume seasonality forecasting engine |
US8600931B1 (en) | 2006-03-31 | 2013-12-03 | Monster Worldwide, Inc. | Apparatuses, methods and systems for automated online data submission |
US8423408B1 (en) | 2006-04-17 | 2013-04-16 | Sprint Communications Company L.P. | Dynamic advertising content distribution and placement systems and methods |
US20070255753A1 (en) * | 2006-05-01 | 2007-11-01 | International Business Machines Corporation | Method, system, and computer program product for providing user-dependent reputation services |
US8442973B2 (en) * | 2006-05-02 | 2013-05-14 | Surf Canyon, Inc. | Real time implicit user modeling for personalized search |
US20070260597A1 (en) * | 2006-05-02 | 2007-11-08 | Mark Cramer | Dynamic search engine results employing user behavior |
US20070288308A1 (en) * | 2006-05-25 | 2007-12-13 | Yahoo Inc. | Method and system for providing job listing affinity |
US7720850B2 (en) * | 2006-05-26 | 2010-05-18 | Campus 1, Inc. | Self-uploaded indexing and data clustering method and apparatus |
US9443022B2 (en) | 2006-06-05 | 2016-09-13 | Google Inc. | Method, system, and graphical user interface for providing personalized recommendations of popular search queries |
US7814112B2 (en) * | 2006-06-09 | 2010-10-12 | Ebay Inc. | Determining relevancy and desirability of terms |
US10410237B1 (en) | 2006-06-26 | 2019-09-10 | Sprint Communications Company L.P. | Inventory management integrating subscriber and targeting data |
US7664744B2 (en) * | 2006-07-14 | 2010-02-16 | Yahoo! Inc. | Query categorizer |
US8301616B2 (en) * | 2006-07-14 | 2012-10-30 | Yahoo! Inc. | Search equalizer |
US20080021900A1 (en) * | 2006-07-14 | 2008-01-24 | Ficus Enterprises, Llc | Examiner information system |
US8442858B1 (en) | 2006-07-21 | 2013-05-14 | Sprint Communications Company L.P. | Subscriber data insertion into advertisement requests |
US7624103B2 (en) | 2006-07-21 | 2009-11-24 | Aol Llc | Culturally relevant search results |
US8364669B1 (en) * | 2006-07-21 | 2013-01-29 | Aol Inc. | Popularity of content items |
WO2008012834A2 (en) * | 2006-07-25 | 2008-01-31 | Jain Pankaj | A method and a system for searching information using information device |
US20080027971A1 (en) * | 2006-07-28 | 2008-01-31 | Craig Statchuk | Method and system for populating an index corpus to a search engine |
WO2008019007A2 (en) * | 2006-08-04 | 2008-02-14 | Thefind, Inc. | Method for relevancy ranking of products in online shopping |
US9779441B1 (en) | 2006-08-04 | 2017-10-03 | Facebook, Inc. | Method for relevancy ranking of products in online shopping |
CN101589385A (en) * | 2006-08-21 | 2009-11-25 | 选择引擎有限公司 | A choice engine |
US8117197B1 (en) | 2008-06-10 | 2012-02-14 | Surf Canyon, Inc. | Adaptive user interface for real-time search relevance feedback |
US7890440B1 (en) | 2006-08-29 | 2011-02-15 | Science Applications International Corporation | Systems and methods for evaluating a group detection tool |
US7774360B2 (en) * | 2006-09-08 | 2010-08-10 | Microsoft Corporation | Building bridges for web query classification |
US7761805B2 (en) * | 2006-09-11 | 2010-07-20 | Yahoo! Inc. | Displaying items using a reduced presentation |
KR100920442B1 (en) * | 2006-09-21 | 2009-10-08 | 삼성전자주식회사 | Methods for searching information in portable terminal |
US20100293490A1 (en) * | 2006-09-26 | 2010-11-18 | Armand Rousso | Apparatuses, Methods and Systems For An Information Comparator Comparison Engine |
US7895127B2 (en) * | 2006-09-29 | 2011-02-22 | Weiser Anatoly S | Rating-based sorting and displaying of reviews |
US8122026B1 (en) | 2006-10-20 | 2012-02-21 | Google Inc. | Finding and disambiguating references to entities on web pages |
US20080104049A1 (en) * | 2006-10-25 | 2008-05-01 | Microsoft Corporation | Document ranking utilizing parameter varying data |
US7493330B2 (en) * | 2006-10-31 | 2009-02-17 | Business Objects Software Ltd. | Apparatus and method for categorical filtering of data |
US7912875B2 (en) | 2006-10-31 | 2011-03-22 | Business Objects Software Ltd. | Apparatus and method for filtering data using nested panels |
US8661029B1 (en) | 2006-11-02 | 2014-02-25 | Google Inc. | Modifying search result ranking based on implicit user feedback |
US9110975B1 (en) | 2006-11-02 | 2015-08-18 | Google Inc. | Search result inputs using variant generalized queries |
US8156112B2 (en) | 2006-11-07 | 2012-04-10 | At&T Intellectual Property I, L.P. | Determining sort order by distance |
US20080109435A1 (en) * | 2006-11-07 | 2008-05-08 | Bellsouth Intellectual Property Corporation | Determining Sort Order by Traffic Volume |
US10664851B1 (en) | 2006-11-08 | 2020-05-26 | Sprint Communications Company, L.P. | Behavioral analysis engine for profiling wireless subscribers |
US10068261B1 (en) | 2006-11-09 | 2018-09-04 | Sprint Communications Company L.P. | In-flight campaign optimization |
US8131722B2 (en) * | 2006-11-20 | 2012-03-06 | Ebay Inc. | Search clustering |
US7630970B2 (en) * | 2006-11-28 | 2009-12-08 | Yahoo! Inc. | Wait timer for partially formed query |
US7921092B2 (en) * | 2006-12-04 | 2011-04-05 | Yahoo! Inc. | Topic-focused search result summaries |
US7630972B2 (en) * | 2007-01-05 | 2009-12-08 | Yahoo! Inc. | Clustered search processing |
US20080154924A1 (en) * | 2006-12-22 | 2008-06-26 | Jun Ding | Dynamic networking by matching profile information |
US8326673B1 (en) | 2006-12-28 | 2012-12-04 | Sprint Communications Company L.P. | Carrier data based product inventory management and marketing |
US8620952B2 (en) | 2007-01-03 | 2013-12-31 | Carhamm Ltd., Llc | System for database reporting |
US20090012841A1 (en) * | 2007-01-05 | 2009-01-08 | Yahoo! Inc. | Event communication platform for mobile device users |
US9177054B2 (en) * | 2007-01-31 | 2015-11-03 | Paypal, Inc. | Selective presentation of data items |
US9449322B2 (en) * | 2007-02-28 | 2016-09-20 | Ebay Inc. | Method and system of suggesting information used with items offered for sale in a network-based marketplace |
US8938463B1 (en) | 2007-03-12 | 2015-01-20 | Google Inc. | Modifying search result ranking based on implicit user feedback and a model of presentation bias |
US8347202B1 (en) | 2007-03-14 | 2013-01-01 | Google Inc. | Determining geographic locations for place names in a fact repository |
US8799250B1 (en) * | 2007-03-26 | 2014-08-05 | Amazon Technologies, Inc. | Enhanced search with user suggested search information |
US7647309B1 (en) * | 2007-03-29 | 2010-01-12 | A9.Com, Inc. | Browse relevance |
US8166045B1 (en) | 2007-03-30 | 2012-04-24 | Google Inc. | Phrase extraction using subphrase scoring |
US8086594B1 (en) | 2007-03-30 | 2011-12-27 | Google Inc. | Bifurcated document relevance scoring |
US7693813B1 (en) | 2007-03-30 | 2010-04-06 | Google Inc. | Index server architecture using tiered and sharded phrase posting lists |
US8166021B1 (en) | 2007-03-30 | 2012-04-24 | Google Inc. | Query phrasification |
US7925655B1 (en) | 2007-03-30 | 2011-04-12 | Google Inc. | Query scheduling using hierarchical tiers of index servers |
US7702614B1 (en) | 2007-03-30 | 2010-04-20 | Google Inc. | Index updating using segment swapping |
US8479116B2 (en) * | 2007-04-13 | 2013-07-02 | Hntb Holdings Ltd | User interface for engineered systems asset analysis |
US8977631B2 (en) | 2007-04-16 | 2015-03-10 | Ebay Inc. | Visualization of reputation ratings |
US9092510B1 (en) | 2007-04-30 | 2015-07-28 | Google Inc. | Modifying search result ranking based on a temporal element of user feedback |
US8239350B1 (en) | 2007-05-08 | 2012-08-07 | Google Inc. | Date ambiguity resolution |
US8768932B1 (en) * | 2007-05-14 | 2014-07-01 | Google Inc. | Method and apparatus for ranking search results |
US7792826B2 (en) * | 2007-05-29 | 2010-09-07 | International Business Machines Corporation | Method and system for providing ranked search results |
US8051040B2 (en) | 2007-06-08 | 2011-11-01 | Ebay Inc. | Electronic publication system |
US20090006179A1 (en) * | 2007-06-26 | 2009-01-01 | Ebay Inc. | Economic optimization for product search relevancy |
US7966291B1 (en) | 2007-06-26 | 2011-06-21 | Google Inc. | Fact-based object merging |
US7783630B1 (en) * | 2007-06-29 | 2010-08-24 | Emc Corporation | Tuning of relevancy ranking for federated search |
US7783620B1 (en) * | 2007-06-29 | 2010-08-24 | Emc Corporation | Relevancy scoring using query structure and data structure for federated search |
US20090024470A1 (en) * | 2007-07-20 | 2009-01-22 | Google Inc. | Vertical clustering and anti-clustering of categories in ad link units |
US7970766B1 (en) | 2007-07-23 | 2011-06-28 | Google Inc. | Entity type assignment |
US8738643B1 (en) | 2007-08-02 | 2014-05-27 | Google Inc. | Learning synonymous object names from anchor texts |
US8312010B1 (en) | 2007-08-16 | 2012-11-13 | Google Inc. | Local business ranking using mapping information |
US8694511B1 (en) | 2007-08-20 | 2014-04-08 | Google Inc. | Modifying search result ranking based on populations |
EP2191401A1 (en) * | 2007-08-27 | 2010-06-02 | Google, Inc. | Distinguishing accessories from products for ranking search results |
US8117223B2 (en) | 2007-09-07 | 2012-02-14 | Google Inc. | Integrating external related phrase information into a phrase-based indexing information retrieval system |
CN101868797B (en) * | 2007-09-21 | 2013-05-01 | 谷歌公司 | Cross-language search |
US8909655B1 (en) | 2007-10-11 | 2014-12-09 | Google Inc. | Time based ranking |
US8126863B2 (en) * | 2007-10-25 | 2012-02-28 | Apple Inc. | Search control combining classification and text-based searching techniques |
US20090119276A1 (en) * | 2007-11-01 | 2009-05-07 | Antoine Sorel Neron | Method and Internet-based Search Engine System for Storing, Sorting, and Displaying Search Results |
US8823709B2 (en) * | 2007-11-01 | 2014-09-02 | Ebay Inc. | User interface framework for viewing large scale graphs on the web |
US8812435B1 (en) | 2007-11-16 | 2014-08-19 | Google Inc. | Learning objects and facts from documents |
US20090144226A1 (en) * | 2007-12-03 | 2009-06-04 | Kei Tateno | Information processing device and method, and program |
US20090144264A1 (en) * | 2007-12-04 | 2009-06-04 | Vik Singh | Third-party information overlay on search results |
US7415460B1 (en) | 2007-12-10 | 2008-08-19 | International Business Machines Corporation | System and method to customize search engine results by picking documents |
US20090164266A1 (en) * | 2007-12-21 | 2009-06-25 | Microsoft Corporation | Category aggregated opinion data |
US7984019B2 (en) * | 2007-12-28 | 2011-07-19 | Knowledge Computing Corporation | Method and apparatus for loading data files into a data-warehouse system |
TW200928798A (en) * | 2007-12-31 | 2009-07-01 | Aletheia University | Method for analyzing technology document |
US8402031B2 (en) * | 2008-01-11 | 2013-03-19 | Microsoft Corporation | Determining entity popularity using search queries |
US8751492B1 (en) * | 2008-01-17 | 2014-06-10 | Amdocs Software Systems Limited | System, method, and computer program product for selecting an event category based on a category score for use in providing content |
US20090216716A1 (en) * | 2008-02-25 | 2009-08-27 | Nokia Corporation | Methods, Apparatuses and Computer Program Products for Providing a Search Form |
US20090265326A1 (en) * | 2008-04-17 | 2009-10-22 | Thomas Dudley Lehrman | Dynamic personal privacy system for internet-connected social networks |
US9779390B1 (en) | 2008-04-21 | 2017-10-03 | Monster Worldwide, Inc. | Apparatuses, methods and systems for advancement path benchmarking |
US8086590B2 (en) | 2008-04-25 | 2011-12-27 | Microsoft Corporation | Product suggestions and bypassing irrelevant query results |
US20090307209A1 (en) * | 2008-06-10 | 2009-12-10 | David Carmel | Term-statistics modification for category-based search |
US20090313202A1 (en) * | 2008-06-13 | 2009-12-17 | Genady Grabarnik | Systems and methods for automated search-based problem determination and resolution for complex systems |
US9323832B2 (en) * | 2008-06-18 | 2016-04-26 | Ebay Inc. | Determining desirability value using sale format of item listing |
US10915523B1 (en) | 2010-05-12 | 2021-02-09 | Richard Paiz | Codex search patterns |
US11048765B1 (en) | 2008-06-25 | 2021-06-29 | Richard Paiz | Search engine optimizer |
US10922363B1 (en) | 2010-04-21 | 2021-02-16 | Richard Paiz | Codex search patterns |
US20090327268A1 (en) * | 2008-06-27 | 2009-12-31 | Microsoft Corporation | Providing targeted information for entertainment-oriented searches |
US8180771B2 (en) | 2008-07-18 | 2012-05-15 | Iac Search & Media, Inc. | Search activity eraser |
US8037050B2 (en) * | 2008-08-02 | 2011-10-11 | Knowledge Computing Corporation | Methods and apparatus for performing multi-data-source, non-ETL queries and entity resolution |
US20100042610A1 (en) * | 2008-08-15 | 2010-02-18 | Microsoft Corporation | Rank documents based on popularity of key metadata |
US8117216B1 (en) | 2008-08-26 | 2012-02-14 | Amazon Technologies, Inc. | Automated selection of item categories for presenting item recommendations |
US20100076979A1 (en) * | 2008-09-05 | 2010-03-25 | Xuejun Wang | Performing search query dimensional analysis on heterogeneous structured data based on relative density |
US8290923B2 (en) * | 2008-09-05 | 2012-10-16 | Yahoo! Inc. | Performing large scale structured search allowing partial schema changes without system downtime |
US20100076952A1 (en) * | 2008-09-05 | 2010-03-25 | Xuejun Wang | Self contained multi-dimensional traffic data reporting and analysis in a large scale search hosting system |
US9317599B2 (en) * | 2008-09-19 | 2016-04-19 | Nokia Technologies Oy | Method, apparatus and computer program product for providing relevance indication |
US9092517B2 (en) * | 2008-09-23 | 2015-07-28 | Microsoft Technology Licensing, Llc | Generating synonyms based on query log data |
US20100082356A1 (en) * | 2008-09-30 | 2010-04-01 | Yahoo! Inc. | System and method for recommending personalized career paths |
US20100121842A1 (en) * | 2008-11-13 | 2010-05-13 | Dennis Klinkott | Method, apparatus and computer program product for presenting categorized search results |
US8392429B1 (en) * | 2008-11-26 | 2013-03-05 | Google Inc. | Informational book query |
US8396865B1 (en) * | 2008-12-10 | 2013-03-12 | Google Inc. | Sharing search engine relevance data between corpora |
US20100161586A1 (en) * | 2008-12-18 | 2010-06-24 | Safar Samir H | System and method of multi-page display and interaction of any internet search engine data on an internet browser |
US9442933B2 (en) * | 2008-12-24 | 2016-09-13 | Comcast Interactive Media, Llc | Identification of segments within audio, video, and multimedia items |
US8713016B2 (en) | 2008-12-24 | 2014-04-29 | Comcast Interactive Media, Llc | Method and apparatus for organizing segments of media assets and determining relevance of segments to a query |
US11531668B2 (en) | 2008-12-29 | 2022-12-20 | Comcast Interactive Media, Llc | Merging of multiple data sets |
US20100169160A1 (en) * | 2008-12-30 | 2010-07-01 | Ebay Inc. | Gift recommendation method and system |
WO2010096986A1 (en) * | 2009-02-27 | 2010-09-02 | 华为技术有限公司 | Mobile search method and device |
US8176043B2 (en) | 2009-03-12 | 2012-05-08 | Comcast Interactive Media, Llc | Ranking search results |
US8244564B2 (en) * | 2009-03-31 | 2012-08-14 | Richrelevance, Inc. | Multi-strategy generation of product recommendations |
US20100257171A1 (en) * | 2009-04-03 | 2010-10-07 | Yahoo! Inc. | Techniques for categorizing search queries |
US9009146B1 (en) | 2009-04-08 | 2015-04-14 | Google Inc. | Ranking search results based on similar queries |
US9412127B2 (en) | 2009-04-08 | 2016-08-09 | Ebay Inc. | Methods and systems for assessing the quality of an item listing |
US10303722B2 (en) * | 2009-05-05 | 2019-05-28 | Oracle America, Inc. | System and method for content selection for web page indexing |
US20100287174A1 (en) * | 2009-05-11 | 2010-11-11 | Yahoo! Inc. | Identifying a level of desirability of hyperlinked information or other user selectable information |
CN101887437B (en) | 2009-05-12 | 2016-03-30 | 阿里巴巴集团控股有限公司 | A kind of Search Results generation method and information search system |
US8533223B2 (en) | 2009-05-12 | 2013-09-10 | Comcast Interactive Media, LLC. | Disambiguation and tagging of entities |
US8234147B2 (en) * | 2009-05-15 | 2012-07-31 | Microsoft Corporation | Multi-variable product rank |
US9141671B2 (en) * | 2009-05-18 | 2015-09-22 | Cbs Interactive Inc. | System and method for presenting filter options to a user based on ongoing monitoring of filter selections |
US20100306191A1 (en) * | 2009-05-27 | 2010-12-02 | Lebeau Michael J | Computer Application Data In Search Results |
US20100306198A1 (en) * | 2009-06-02 | 2010-12-02 | Cbs Interactive, Inc. | System and method for determining categories associated with searches of electronic catalogs and displaying category information with search results |
US9892730B2 (en) | 2009-07-01 | 2018-02-13 | Comcast Interactive Media, Llc | Generating topic-specific language models |
US8447760B1 (en) | 2009-07-20 | 2013-05-21 | Google Inc. | Generating a related set of documents for an initial set of documents |
US8498974B1 (en) | 2009-08-31 | 2013-07-30 | Google Inc. | Refining search results |
US8463769B1 (en) * | 2009-09-16 | 2013-06-11 | Amazon Technologies, Inc. | Identifying missing search phrases |
US9846898B2 (en) * | 2009-09-30 | 2017-12-19 | Ebay Inc. | Method and system for exposing data used in ranking search results |
US8972391B1 (en) | 2009-10-02 | 2015-03-03 | Google Inc. | Recent interest based relevance scoring |
US8301512B2 (en) | 2009-10-23 | 2012-10-30 | Ebay Inc. | Product identification using multiple services |
US8577879B1 (en) * | 2009-10-29 | 2013-11-05 | Amazon Technologies, Inc. | Navigational aids within item search results |
CN102056335B (en) * | 2009-11-06 | 2014-02-19 | 华为技术有限公司 | Mobile search method, device and system |
CN102063432A (en) | 2009-11-12 | 2011-05-18 | 阿里巴巴集团控股有限公司 | Retrieval method and retrieval system |
US8874555B1 (en) | 2009-11-20 | 2014-10-28 | Google Inc. | Modifying scoring data based on historical changes |
TWI493366B (en) * | 2010-02-11 | 2015-07-21 | Alibaba Group Holding Ltd | Retrieval methods and systems |
US8924379B1 (en) | 2010-03-05 | 2014-12-30 | Google Inc. | Temporal-based score adjustments |
US20110218986A1 (en) * | 2010-03-06 | 2011-09-08 | David Joseph O'Hanlon | Search engine optimization economic purchasing method |
US8959093B1 (en) | 2010-03-15 | 2015-02-17 | Google Inc. | Ranking search results based on anchors |
US9984150B2 (en) * | 2010-03-26 | 2018-05-29 | Ebay Inc. | Category management and analysis |
US20110258187A1 (en) * | 2010-04-14 | 2011-10-20 | Raytheon Company | Relevance-Based Open Source Intelligence (OSINT) Collection |
US11423018B1 (en) * | 2010-04-21 | 2022-08-23 | Richard Paiz | Multivariate analysis replica intelligent ambience evolving system |
US10936687B1 (en) * | 2010-04-21 | 2021-03-02 | Richard Paiz | Codex search patterns virtual maestro |
US11379473B1 (en) * | 2010-04-21 | 2022-07-05 | Richard Paiz | Site rank codex search patterns |
US8452765B2 (en) * | 2010-04-23 | 2013-05-28 | Eye Level Holdings, Llc | System and method of controlling interactive communication services by responding to user query with relevant information from content specific database |
US9600566B2 (en) | 2010-05-14 | 2017-03-21 | Microsoft Technology Licensing, Llc | Identifying entity synonyms |
US8612432B2 (en) * | 2010-06-16 | 2013-12-17 | Microsoft Corporation | Determining query intent |
US9275154B2 (en) | 2010-06-18 | 2016-03-01 | Google Inc. | Context-sensitive point of interest retrieval |
US9715553B1 (en) | 2010-06-18 | 2017-07-25 | Google Inc. | Point of interest retrieval |
US9194716B1 (en) * | 2010-06-18 | 2015-11-24 | Google Inc. | Point of interest category ranking |
US9623119B1 (en) | 2010-06-29 | 2017-04-18 | Google Inc. | Accentuating search results |
CN102314450B (en) * | 2010-06-30 | 2014-11-26 | 国际商业机器公司 | Method for enhancing webpage browse and equipment |
US10748159B1 (en) | 2010-07-08 | 2020-08-18 | Richrelevance, Inc. | Contextual analysis and control of content item selection |
US8832083B1 (en) | 2010-07-23 | 2014-09-09 | Google Inc. | Combining user feedback |
US8463805B2 (en) * | 2010-09-15 | 2013-06-11 | Cbs Interactive, Inc. | Mapping product identification information to a product |
JP5542017B2 (en) * | 2010-09-15 | 2014-07-09 | アルパイン株式会社 | Name search device |
CN102541857A (en) * | 2010-12-08 | 2012-07-04 | 腾讯科技(深圳)有限公司 | Webpage sorting method and device |
US20120151390A1 (en) * | 2010-12-13 | 2012-06-14 | Sony Corporation | User interface for media content presentation |
US9251185B2 (en) | 2010-12-15 | 2016-02-02 | Girish Kumar | Classifying results of search queries |
US20120158765A1 (en) * | 2010-12-15 | 2012-06-21 | Microsoft Corporation | User Interface for Interactive Query Reformulation |
US9002867B1 (en) | 2010-12-30 | 2015-04-07 | Google Inc. | Modifying ranking data based on document changes |
CN102591876A (en) * | 2011-01-14 | 2012-07-18 | 阿里巴巴集团控股有限公司 | Sequencing method and device of search results |
US20120191508A1 (en) | 2011-01-20 | 2012-07-26 | John Nicholas Gross | System & Method For Predicting Outcome Of An Intellectual Property Rights Proceeding/Challenge |
US10055766B1 (en) * | 2011-02-14 | 2018-08-21 | PayAsOne Intellectual Property Utilization LLC | Viral marketing object oriented system and method |
US9424357B1 (en) | 2011-03-01 | 2016-08-23 | Amazon Technologies, Inc. | Predictive page loading based on text entry and search term suggestions |
US9436726B2 (en) | 2011-06-23 | 2016-09-06 | BCM International Regulatory Analytics LLC | System, method and computer program product for a behavioral database providing quantitative analysis of cross border policy process and related search capabilities |
US8620891B1 (en) * | 2011-06-29 | 2013-12-31 | Amazon Technologies, Inc. | Ranking item attribute refinements |
US9060062B1 (en) | 2011-07-06 | 2015-06-16 | Google Inc. | Clustering and classification of recent customer support inquiries |
US8788925B1 (en) * | 2011-07-21 | 2014-07-22 | Flipboard, Inc. | Authorized syndicated descriptions of linked web content displayed with links in user-generated content |
US9996626B1 (en) | 2011-10-26 | 2018-06-12 | Richrelevance, Inc. | Selection of content item recommendations based on user search results |
JP6077739B2 (en) * | 2011-11-15 | 2017-02-08 | 任天堂株式会社 | Information processing apparatus, information processing system, information processing method, and program |
US8498984B1 (en) * | 2011-11-21 | 2013-07-30 | Google Inc. | Categorization of search results |
US8484099B1 (en) | 2012-02-06 | 2013-07-09 | Amazon Technologies, Inc. | Method, medium, and system for behavior-based recommendations of product upgrades |
JP5880101B2 (en) * | 2012-02-10 | 2016-03-08 | ソニー株式会社 | Information processing apparatus, information processing method, and program |
US20130212089A1 (en) * | 2012-02-10 | 2013-08-15 | Google Inc. | Search Result Categorization |
US8745019B2 (en) | 2012-03-05 | 2014-06-03 | Microsoft Corporation | Robust discovery of entity synonyms using query logs |
US8688713B1 (en) * | 2012-03-22 | 2014-04-01 | Google Inc. | Resource identification from organic and structured content |
US20130282714A1 (en) * | 2012-04-18 | 2013-10-24 | Yumber, Inc. | Personalized Redirection Identifiers |
CN107066509B (en) | 2012-04-26 | 2020-12-04 | 阿里巴巴集团控股有限公司 | Information providing method, processing server and merge server |
US8762368B1 (en) * | 2012-04-30 | 2014-06-24 | Google Inc. | Context-based filtering of search results |
CN103425659B (en) * | 2012-05-15 | 2017-06-09 | 阿里巴巴集团控股有限公司 | Information search method and server based on geographical position |
US9141674B2 (en) * | 2012-05-16 | 2015-09-22 | Google Inc. | Prominent display of selective results of book search queries |
US20130332450A1 (en) * | 2012-06-11 | 2013-12-12 | International Business Machines Corporation | System and Method for Automatically Detecting and Interactively Displaying Information About Entities, Activities, and Events from Multiple-Modality Natural Language Sources |
US10032131B2 (en) | 2012-06-20 | 2018-07-24 | Microsoft Technology Licensing, Llc | Data services for enterprises leveraging search system data assets |
US9594831B2 (en) | 2012-06-22 | 2017-03-14 | Microsoft Technology Licensing, Llc | Targeted disambiguation of named entities |
US9348846B2 (en) * | 2012-07-02 | 2016-05-24 | Google Inc. | User-navigable resource representations |
US20140047359A1 (en) * | 2012-08-08 | 2014-02-13 | Arnstein Osnes Teigene | Mechanism for adding new search modes to user agent |
US9229924B2 (en) | 2012-08-24 | 2016-01-05 | Microsoft Technology Licensing, Llc | Word detection and domain dictionary recommendation |
CN103631823B (en) * | 2012-08-28 | 2017-01-18 | 腾讯科技(深圳)有限公司 | Method and device for recommending media content |
JP5741542B2 (en) * | 2012-09-20 | 2015-07-01 | カシオ計算機株式会社 | Information display control device, information display control method, and program |
WO2014081824A2 (en) * | 2012-11-21 | 2014-05-30 | Blekko, Inc. | Search engine results |
US10528907B2 (en) * | 2012-12-19 | 2020-01-07 | Oath Inc. | Automated categorization of products in a merchant catalog |
US9449095B1 (en) * | 2012-12-31 | 2016-09-20 | Google Inc. | Revising search queries |
US9881100B2 (en) * | 2013-01-14 | 2018-01-30 | International Business Machines Corporation | Scoping searches within websites |
US11809506B1 (en) * | 2013-02-26 | 2023-11-07 | Richard Paiz | Multivariant analyzing replicating intelligent ambience evolving system |
US11741090B1 (en) | 2013-02-26 | 2023-08-29 | Richard Paiz | Site rank codex search patterns |
US9501506B1 (en) | 2013-03-15 | 2016-11-22 | Google Inc. | Indexing system |
US9477759B2 (en) | 2013-03-15 | 2016-10-25 | Google Inc. | Question answering using entity references in unstructured data |
US20140289269A1 (en) * | 2013-03-22 | 2014-09-25 | Tencent Technology (Shenzhen) Company Limited | Method, Device and System for Data Searching |
GB201306937D0 (en) * | 2013-04-17 | 2013-05-29 | Tomtom Int Bv | Methods, devices and computer software for facilitating searching and display of locations relevant to a digital map |
US9183499B1 (en) | 2013-04-19 | 2015-11-10 | Google Inc. | Evaluating quality based on neighbor features |
US9524319B2 (en) | 2013-04-30 | 2016-12-20 | Wal-Mart Stores, Inc. | Search relevance |
US9524520B2 (en) | 2013-04-30 | 2016-12-20 | Wal-Mart Stores, Inc. | Training a classification model to predict categories |
US20140324851A1 (en) * | 2013-04-30 | 2014-10-30 | Wal-Mart Stores, Inc. | Classifying e-commerce queries to generate category mappings for dominant products |
GB201308974D0 (en) * | 2013-05-17 | 2013-07-03 | Dun Laoghaire Inst Of Art Design And Technology | System and method for searching information in databases |
US10405173B1 (en) | 2013-06-05 | 2019-09-03 | Sprint Communications Company L.P. | Method and systems of collecting and segmenting device sensor data while in transit via a network |
US9483568B1 (en) | 2013-06-05 | 2016-11-01 | Google Inc. | Indexing system |
US9245026B1 (en) * | 2013-06-26 | 2016-01-26 | Amazon Technologies, Inc. | Increasing the relevancy of search results across categories |
US10268762B1 (en) * | 2013-07-19 | 2019-04-23 | Open Invention Network Llc | Color based search application interface and corresponding query control functions |
US9319379B1 (en) | 2013-08-01 | 2016-04-19 | Sprint Communications Company L.P. | Methods and systems of generating a unique mobile device identifier |
EP3039581A4 (en) | 2013-08-29 | 2016-08-10 | Yandex Europe Ag | A system and method for displaying of most relevant vertical search results |
US9590938B1 (en) | 2013-09-11 | 2017-03-07 | Sprint Communications Company L.P. | System and method for identifying a mobile device with near real time visualization to action |
US9374335B2 (en) | 2013-09-11 | 2016-06-21 | Sprint Communications Company L.P. | System and method for distributing messages to particular mobile devices |
US20150095194A1 (en) * | 2013-09-27 | 2015-04-02 | Romualdas Maslovskis | Systems and methods for search term prioritization |
WO2015063881A1 (en) * | 2013-10-30 | 2015-05-07 | 楽天株式会社 | Information providing device, information providing method, program, and recording medium |
US10423999B1 (en) | 2013-11-01 | 2019-09-24 | Richrelevance, Inc. | Performing personalized category-based product sorting |
US10410241B1 (en) | 2013-11-27 | 2019-09-10 | Sprint Communications Company L.P. | Swipe screen advertisement metrics and tracking |
US9734515B1 (en) | 2014-01-09 | 2017-08-15 | Sprint Communications Company L.P. | Ad management using ads cached on a mobile electronic device |
US10311095B2 (en) * | 2014-01-17 | 2019-06-04 | Renée BUNNELL | Method and system for qualitatively and quantitatively analyzing experiences for recommendation profiles |
US9984395B1 (en) | 2014-01-21 | 2018-05-29 | Sprint Communications Company L.P. | Advertisement mediation of supply-demand communications |
US10055757B1 (en) | 2014-01-21 | 2018-08-21 | Sprint Communications Company L.P. | IP address hashing in advertisement gateway |
US10013707B1 (en) | 2014-01-21 | 2018-07-03 | Sprint Communications Company L.P. | Address modification for advertisement mediation |
US9836771B1 (en) | 2014-01-21 | 2017-12-05 | Sprint Communications Company L.P. | Client mediation and integration to advertisement gateway |
US10380204B1 (en) * | 2014-02-12 | 2019-08-13 | Pinterest, Inc. | Visual search |
US10146872B2 (en) * | 2014-07-16 | 2018-12-04 | Excalibur Ip, Llc | Method and system for predicting search results quality in vertical ranking |
US20160026341A1 (en) * | 2014-07-22 | 2016-01-28 | Igor Kraev | Matrix interface for enabling access to digital content |
US9298796B2 (en) * | 2014-08-01 | 2016-03-29 | Veeva Systems Inc. | System and method for enterprise data management |
RU2610279C2 (en) * | 2014-08-13 | 2017-02-08 | Общество С Ограниченной Ответственностью "Яндекс" | Search method (versions), server and electronic device |
US9508090B1 (en) | 2014-09-24 | 2016-11-29 | Sprint Communications Company L.P. | End user participation in mobile advertisement |
US9818133B1 (en) | 2014-10-20 | 2017-11-14 | Sprint Communications Company L.P. | Method for consumer profile consolidation using mobile network identification |
US20160125028A1 (en) * | 2014-11-05 | 2016-05-05 | Yahoo! Inc. | Systems and methods for query rewriting |
US10002165B2 (en) * | 2014-11-07 | 2018-06-19 | International Business Machines Corporation | Holistic federated ranking algorithm |
WO2016081749A1 (en) * | 2014-11-19 | 2016-05-26 | Google Inc. | Methods, systems, and media for presenting related media content items |
US10726449B2 (en) * | 2014-12-16 | 2020-07-28 | Arkadiy I Rozental | System and method for extracting and publishing information captured by point of sale, point of service, and other order management and sales transactions recording systems for positively influencing internet search engine results |
US10366432B2 (en) * | 2015-02-26 | 2019-07-30 | Walmart Apollo, Llc | System, method, and non-transitory computer-readable storage media for displaying product information on websites |
US10032205B2 (en) | 2015-03-27 | 2018-07-24 | Walmart Apollo, Llc | System, method, and non-transitory computer-readable storage media for displaying product information on websites |
EP3292532A1 (en) * | 2015-05-04 | 2018-03-14 | Contextlogic Inc. | Systems and techniques for presenting and rating items in an online marketplace |
US10127285B2 (en) * | 2015-07-22 | 2018-11-13 | Ariba, Inc. | Customizable ranking of search engine results in multi-tenant architecture |
US9632676B1 (en) * | 2015-10-14 | 2017-04-25 | Twiggle Ltd. | Systems and methods for navigating a set of data objects |
WO2017070672A1 (en) * | 2015-10-23 | 2017-04-27 | John Cameron | Methods and systems for targeted advertising |
US10534783B1 (en) | 2016-02-08 | 2020-01-14 | Microstrategy Incorporated | Enterprise search |
US10339564B2 (en) * | 2016-02-16 | 2019-07-02 | Oracle International Corporation | System and method for providing an adaptively ordered presentation of objects |
US10440139B2 (en) * | 2016-03-11 | 2019-10-08 | International Business Machines Corporation | Process broker for executing web services in a system of engagement and system of record environments |
US20170316483A1 (en) * | 2016-04-29 | 2017-11-02 | Ebay Inc. | Generating a personalized list of items |
US11042595B2 (en) * | 2016-05-04 | 2021-06-22 | Ebay Inc. | Dissimilar but relevant search engine results |
US10789278B1 (en) * | 2016-06-30 | 2020-09-29 | Costar Realty Information, Inc. | Database search engine optimization |
US10102255B2 (en) * | 2016-09-08 | 2018-10-16 | Facebook, Inc. | Categorizing objects for queries on online social networks |
US10268734B2 (en) * | 2016-09-30 | 2019-04-23 | International Business Machines Corporation | Providing search results based on natural language classification confidence information |
CN108182186B (en) * | 2016-12-08 | 2020-10-02 | 广东精点数据科技股份有限公司 | Webpage sorting method based on random forest algorithm |
CN108230057A (en) * | 2016-12-09 | 2018-06-29 | 阿里巴巴集团控股有限公司 | A kind of intelligent recommendation method and system |
US11580115B2 (en) | 2017-06-29 | 2023-02-14 | Ebay Inc. | Identification of intent and non-intent query portions |
US11294911B2 (en) * | 2017-09-29 | 2022-04-05 | Apple Inc. | Methods and systems for client side search ranking improvements |
US10769164B2 (en) * | 2017-12-06 | 2020-09-08 | Sap Se | Simplified access for core business with enterprise search |
US10866976B1 (en) * | 2018-03-20 | 2020-12-15 | Amazon Technologies, Inc. | Categorical exploration facilitation responsive to broad search queries |
CN108984675B (en) * | 2018-07-02 | 2019-08-13 | 北京百度网讯科技有限公司 | Data query method and apparatus based on evaluation |
US10891676B1 (en) | 2018-07-10 | 2021-01-12 | Amazon Technologies, Inc. | User interface for navigating related items |
US11435873B1 (en) | 2018-12-12 | 2022-09-06 | Amazon Technologies, Inc. | User interface for identifying substitute items |
US11620342B2 (en) * | 2019-03-28 | 2023-04-04 | Verizon Patent And Licensing Inc. | Relevance-based search and discovery for media content delivery |
US11386476B2 (en) | 2019-11-11 | 2022-07-12 | Shopify Inc. | Methods and systems for notifying users of new applications |
US11010156B1 (en) | 2019-11-11 | 2021-05-18 | Shopify Inc. | Methods and systems for generating application build recommendations |
CN112767101A (en) * | 2021-01-27 | 2021-05-07 | 南方电网深圳数字电网研究院有限公司 | One-key label clearing method, system and storage medium |
CN113689260B (en) * | 2021-08-18 | 2023-08-08 | 青岛海信智慧生活科技股份有限公司 | Commodity searching method and device |
CN114286169B (en) * | 2021-08-31 | 2023-06-20 | 腾讯科技(深圳)有限公司 | Video generation method, device, terminal, server and storage medium |
Citations (28)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5754938A (en) * | 1994-11-29 | 1998-05-19 | Herz; Frederick S. M. | Pseudonymous server for system for customized electronic identification of desirable objects |
US5845278A (en) * | 1997-09-12 | 1998-12-01 | Inioseek Corporation | Method for automatically selecting collections to search in full text searches |
US5848396A (en) * | 1996-04-26 | 1998-12-08 | Freedom Of Information, Inc. | Method and apparatus for determining behavioral profile of a computer user |
US5895454A (en) * | 1997-04-17 | 1999-04-20 | Harrington; Juliette | Integrated interface for vendor/product oriented internet websites |
US5913210A (en) * | 1998-03-27 | 1999-06-15 | Call; Charles G. | Methods and apparatus for disseminating product information via the internet |
US5918214A (en) * | 1996-10-25 | 1999-06-29 | Ipf, Inc. | System and method for finding product and service related information on the internet |
US6003027A (en) * | 1997-11-21 | 1999-12-14 | International Business Machines Corporation | System and method for determining confidence levels for the results of a categorization system |
US6006225A (en) * | 1998-06-15 | 1999-12-21 | Amazon.Com | Refining search queries by the suggestion of correlated terms from prior searches |
US6009459A (en) * | 1997-01-10 | 1999-12-28 | Microsoft Corporation | Intelligent automatic searching for resources in a distributed environment |
US6038560A (en) * | 1997-05-21 | 2000-03-14 | Oracle Corporation | Concept knowledge base search and retrieval system |
US6182068B1 (en) * | 1997-08-01 | 2001-01-30 | Ask Jeeves, Inc. | Personalized search methods |
US6199099B1 (en) * | 1999-03-05 | 2001-03-06 | Ac Properties B.V. | System, method and article of manufacture for a mobile communication network utilizing a distributed communication network |
US6223145B1 (en) * | 1997-11-26 | 2001-04-24 | Zerox Corporation | Interactive interface for specifying searches |
US6336112B2 (en) * | 1998-08-29 | 2002-01-01 | International Business Machines Corporation | Method for interactively creating an information database including preferred information elements, such as, preferred-authority, world wide web pages |
US6393415B1 (en) * | 1999-03-31 | 2002-05-21 | Verizon Laboratories Inc. | Adaptive partitioning techniques in performing query requests and request routing |
US6438579B1 (en) * | 1999-07-16 | 2002-08-20 | Agent Arts, Inc. | Automated content and collaboration-based system and methods for determining and providing content recommendations |
US6460029B1 (en) * | 1998-12-23 | 2002-10-01 | Microsoft Corporation | System for improving search text |
US6460034B1 (en) * | 1997-05-21 | 2002-10-01 | Oracle Corporation | Document knowledge base research and retrieval system |
US20020165849A1 (en) * | 1999-05-28 | 2002-11-07 | Singh Narinder Pal | Automatic advertiser notification for a system for providing place and price protection in a search result list generated by a computer network search engine |
US6571234B1 (en) * | 1999-05-11 | 2003-05-27 | Prophet Financial Systems, Inc. | System and method for managing online message board |
US20030195877A1 (en) * | 1999-12-08 | 2003-10-16 | Ford James L. | Search query processing to provide category-ranked presentation of search results |
US6804675B1 (en) * | 1999-05-11 | 2004-10-12 | Maquis Techtrix, Llc | Online content provider system and method |
US6826559B1 (en) * | 1999-03-31 | 2004-11-30 | Verizon Laboratories Inc. | Hybrid category mapping for on-line query tool |
US6990628B1 (en) * | 1999-06-14 | 2006-01-24 | Yahoo! Inc. | Method and apparatus for measuring similarity among electronic documents |
US7031961B2 (en) * | 1999-05-05 | 2006-04-18 | Google, Inc. | System and method for searching and recommending objects from a categorically organized information repository |
US7047242B1 (en) * | 1999-03-31 | 2006-05-16 | Verizon Laboratories Inc. | Weighted term ranking for on-line query tool |
US7072888B1 (en) * | 1999-06-16 | 2006-07-04 | Triogo, Inc. | Process for improving search engine efficiency using feedback |
US7076504B1 (en) * | 1998-11-19 | 2006-07-11 | Accenture Llp | Sharing a centralized profile |
Family Cites Families (18)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5675819A (en) * | 1994-06-16 | 1997-10-07 | Xerox Corporation | Document information retrieval using global word co-occurrence patterns |
US5640553A (en) * | 1995-09-15 | 1997-06-17 | Infonautics Corporation | Relevance normalization for documents retrieved from an information retrieval system in response to a query |
DE69624809T2 (en) | 1996-08-28 | 2003-07-03 | Koninkl Philips Electronics Nv | Method and system for selecting an information item |
GB2331166B (en) | 1997-11-06 | 2002-09-11 | Ibm | Database search engine |
US6014664A (en) * | 1997-08-29 | 2000-01-11 | International Business Machines Corporation | Method and apparatus for incorporating weights into data combinational rules |
AU9228298A (en) | 1997-09-12 | 1999-04-05 | Infoseek Corporation | Methods for iteratively and interactively performing collection selection in full text searches |
EP1062602B8 (en) | 1998-02-13 | 2018-06-13 | Oath Inc. | Search engine using sales and revenue to weight search results |
US6185558B1 (en) * | 1998-03-03 | 2001-02-06 | Amazon.Com, Inc. | Identifying the items most relevant to a current query based on items selected in connection with similar queries |
EP1060449B1 (en) | 1998-03-03 | 2003-06-25 | Amazon.Com, Inc. | Identifying the items most relevant to a current query based on items selected in connection with similar queries |
US6073135A (en) * | 1998-03-10 | 2000-06-06 | Alta Vista Company | Connectivity server for locating linkage information between Web pages |
US6421675B1 (en) * | 1998-03-16 | 2002-07-16 | S. L. I. Systems, Inc. | Search engine |
US6032145A (en) * | 1998-04-10 | 2000-02-29 | Requisite Technology, Inc. | Method and system for database manipulation |
US6182050B1 (en) * | 1998-05-28 | 2001-01-30 | Acceleration Software International Corporation | Advertisements distributed on-line using target criteria screening with method for maintaining end user privacy |
US6317722B1 (en) * | 1998-09-18 | 2001-11-13 | Amazon.Com, Inc. | Use of electronic shopping carts to generate personal recommendations |
US6366910B1 (en) * | 1998-12-07 | 2002-04-02 | Amazon.Com, Inc. | Method and system for generation of hierarchical search results |
US6442544B1 (en) * | 1998-12-08 | 2002-08-27 | Infospace, Inc. | System and method for organizing search categories for use in an on-line search query engine based on geographic descriptions |
US6125395A (en) * | 1999-10-04 | 2000-09-26 | Piiq.Com, Inc. | Method for identifying collections of internet web sites with domain names |
US8001118B2 (en) * | 2001-03-02 | 2011-08-16 | Google Inc. | Methods and apparatus for employing usage statistics in document retrieval |
-
2003
- 2003-03-31 US US10/404,992 patent/US6963867B2/en not_active Expired - Lifetime
-
2005
- 2005-07-21 US US11/186,403 patent/US20050289140A1/en not_active Abandoned
Patent Citations (30)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5754938A (en) * | 1994-11-29 | 1998-05-19 | Herz; Frederick S. M. | Pseudonymous server for system for customized electronic identification of desirable objects |
US5848396A (en) * | 1996-04-26 | 1998-12-08 | Freedom Of Information, Inc. | Method and apparatus for determining behavioral profile of a computer user |
US5918214A (en) * | 1996-10-25 | 1999-06-29 | Ipf, Inc. | System and method for finding product and service related information on the internet |
US6009459A (en) * | 1997-01-10 | 1999-12-28 | Microsoft Corporation | Intelligent automatic searching for resources in a distributed environment |
US5895454A (en) * | 1997-04-17 | 1999-04-20 | Harrington; Juliette | Integrated interface for vendor/product oriented internet websites |
US6460034B1 (en) * | 1997-05-21 | 2002-10-01 | Oracle Corporation | Document knowledge base research and retrieval system |
US6038560A (en) * | 1997-05-21 | 2000-03-14 | Oracle Corporation | Concept knowledge base search and retrieval system |
US20030187837A1 (en) * | 1997-08-01 | 2003-10-02 | Ask Jeeves, Inc. | Personalized search method |
US6182068B1 (en) * | 1997-08-01 | 2001-01-30 | Ask Jeeves, Inc. | Personalized search methods |
US5845278A (en) * | 1997-09-12 | 1998-12-01 | Inioseek Corporation | Method for automatically selecting collections to search in full text searches |
US6003027A (en) * | 1997-11-21 | 1999-12-14 | International Business Machines Corporation | System and method for determining confidence levels for the results of a categorization system |
US6223145B1 (en) * | 1997-11-26 | 2001-04-24 | Zerox Corporation | Interactive interface for specifying searches |
US5913210A (en) * | 1998-03-27 | 1999-06-15 | Call; Charles G. | Methods and apparatus for disseminating product information via the internet |
US6006225A (en) * | 1998-06-15 | 1999-12-21 | Amazon.Com | Refining search queries by the suggestion of correlated terms from prior searches |
US6336112B2 (en) * | 1998-08-29 | 2002-01-01 | International Business Machines Corporation | Method for interactively creating an information database including preferred information elements, such as, preferred-authority, world wide web pages |
US7076504B1 (en) * | 1998-11-19 | 2006-07-11 | Accenture Llp | Sharing a centralized profile |
US6460029B1 (en) * | 1998-12-23 | 2002-10-01 | Microsoft Corporation | System for improving search text |
US6199099B1 (en) * | 1999-03-05 | 2001-03-06 | Ac Properties B.V. | System, method and article of manufacture for a mobile communication network utilizing a distributed communication network |
US6393415B1 (en) * | 1999-03-31 | 2002-05-21 | Verizon Laboratories Inc. | Adaptive partitioning techniques in performing query requests and request routing |
US6643640B1 (en) * | 1999-03-31 | 2003-11-04 | Verizon Laboratories Inc. | Method for performing a data query |
US6826559B1 (en) * | 1999-03-31 | 2004-11-30 | Verizon Laboratories Inc. | Hybrid category mapping for on-line query tool |
US7047242B1 (en) * | 1999-03-31 | 2006-05-16 | Verizon Laboratories Inc. | Weighted term ranking for on-line query tool |
US7031961B2 (en) * | 1999-05-05 | 2006-04-18 | Google, Inc. | System and method for searching and recommending objects from a categorically organized information repository |
US6571234B1 (en) * | 1999-05-11 | 2003-05-27 | Prophet Financial Systems, Inc. | System and method for managing online message board |
US6804675B1 (en) * | 1999-05-11 | 2004-10-12 | Maquis Techtrix, Llc | Online content provider system and method |
US20020165849A1 (en) * | 1999-05-28 | 2002-11-07 | Singh Narinder Pal | Automatic advertiser notification for a system for providing place and price protection in a search result list generated by a computer network search engine |
US6990628B1 (en) * | 1999-06-14 | 2006-01-24 | Yahoo! Inc. | Method and apparatus for measuring similarity among electronic documents |
US7072888B1 (en) * | 1999-06-16 | 2006-07-04 | Triogo, Inc. | Process for improving search engine efficiency using feedback |
US6438579B1 (en) * | 1999-07-16 | 2002-08-20 | Agent Arts, Inc. | Automated content and collaboration-based system and methods for determining and providing content recommendations |
US20030195877A1 (en) * | 1999-12-08 | 2003-10-16 | Ford James L. | Search query processing to provide category-ranked presentation of search results |
Cited By (163)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7346605B1 (en) * | 1999-07-22 | 2008-03-18 | Markmonitor, Inc. | Method and system for searching and monitoring internet trademark usage |
US7096214B1 (en) | 1999-12-15 | 2006-08-22 | Google Inc. | System and method for supporting editorial opinion in the ranking of search results |
US7386543B1 (en) | 1999-12-15 | 2008-06-10 | Google Inc. | System and method for supporting editorial opinion in the ranking of search results |
US7464086B2 (en) * | 2000-08-01 | 2008-12-09 | Yahoo! Inc. | Metatag-based datamining |
US20020035573A1 (en) * | 2000-08-01 | 2002-03-21 | Black Peter M. | Metatag-based datamining |
US10496652B1 (en) | 2002-09-20 | 2019-12-03 | Google Llc | Methods and apparatus for ranking documents |
US8225190B1 (en) | 2002-09-20 | 2012-07-17 | Google Inc. | Methods and apparatus for clustering news content |
US7568148B1 (en) | 2002-09-20 | 2009-07-28 | Google Inc. | Methods and apparatus for clustering news content |
US10095752B1 (en) | 2002-09-20 | 2018-10-09 | Google Llc | Methods and apparatus for clustering news online content based on content freshness and quality of content source |
US9477714B1 (en) | 2002-09-20 | 2016-10-25 | Google Inc. | Methods and apparatus for ranking documents |
US9361369B1 (en) | 2002-09-20 | 2016-06-07 | Google Inc. | Method and apparatus for clustering news online content based on content freshness and quality of content source |
US8843479B1 (en) | 2002-09-20 | 2014-09-23 | Google Inc. | Methods and apparatus for ranking documents |
US8090717B1 (en) | 2002-09-20 | 2012-01-03 | Google Inc. | Methods and apparatus for ranking documents |
US20050015307A1 (en) * | 2003-04-28 | 2005-01-20 | Simpson Todd Garrett | Method and system of providing location sensitive business information to customers |
US20040220903A1 (en) * | 2003-04-30 | 2004-11-04 | Emarkmonitor Inc. | Method and system to correlate trademark data to internet domain name data |
US20140188859A1 (en) * | 2003-09-16 | 2014-07-03 | Google Inc. | Systems and methods for improving the ranking of news articles |
US8645368B2 (en) * | 2003-09-16 | 2014-02-04 | Google Inc. | Systems and methods for improving the ranking of news articles |
US9037575B2 (en) * | 2003-09-16 | 2015-05-19 | Google Inc. | Systems and methods for improving the ranking of news articles |
US10459926B2 (en) | 2003-09-16 | 2019-10-29 | Google Llc | Systems and methods for improving the ranking of news articles |
US8332382B2 (en) | 2003-09-16 | 2012-12-11 | Google Inc. | Systems and methods for improving the ranking of news articles |
US7577655B2 (en) * | 2003-09-16 | 2009-08-18 | Google Inc. | Systems and methods for improving the ranking of news articles |
US20130159294A1 (en) * | 2003-09-16 | 2013-06-20 | Google Inc. | Systems and methods for improving the ranking of news articles |
US8126876B2 (en) * | 2003-09-16 | 2012-02-28 | Google Inc. | Systems and methods for improving the ranking of news articles |
US20050060312A1 (en) * | 2003-09-16 | 2005-03-17 | Michael Curtiss | Systems and methods for improving the ranking of news articles |
US20090276429A1 (en) * | 2003-09-16 | 2009-11-05 | Google Inc. | Systems and methods for improving the ranking of news articles |
US20050131872A1 (en) * | 2003-12-16 | 2005-06-16 | Microsoft Corporation | Query recognizer |
US8356278B2 (en) * | 2004-03-31 | 2013-01-15 | International Business Machines Corporation | Method, system and program product for detecting deviation from software development best practice resource in a code sharing system |
US20100005446A1 (en) * | 2004-03-31 | 2010-01-07 | Youssef Drissi | Method, system and program product for detecting deviation from software development best practice resource in a code sharing system |
US10991023B2 (en) | 2004-12-23 | 2021-04-27 | Ebay Inc. | Multiple format search result sets |
US10430853B2 (en) | 2004-12-23 | 2019-10-01 | Ebay Inc. | Multiple format search result sets |
US7849070B2 (en) * | 2005-08-03 | 2010-12-07 | Yahoo! Inc. | System and method for dynamically ranking items of audio content |
US20070033228A1 (en) * | 2005-08-03 | 2007-02-08 | Ethan Fassett | System and method for dynamically ranking items of audio content |
US7693901B2 (en) * | 2005-08-10 | 2010-04-06 | Microsoft Corporation | Consumer-focused results ordering |
US20070038620A1 (en) * | 2005-08-10 | 2007-02-15 | Microsoft Corporation | Consumer-focused results ordering |
US8719255B1 (en) | 2005-08-23 | 2014-05-06 | Amazon Technologies, Inc. | Method and system for determining interest levels of online content based on rates of change of content access |
US7774335B1 (en) * | 2005-08-23 | 2010-08-10 | Amazon Technologies, Inc. | Method and system for determining interest levels of online content navigation paths |
US8370342B1 (en) * | 2005-09-27 | 2013-02-05 | Google Inc. | Display of relevant results |
US20070073658A1 (en) * | 2005-09-29 | 2007-03-29 | Faerber Franz X | Efficient handling of multipart queries against relational data |
US7739285B2 (en) * | 2005-09-29 | 2010-06-15 | Sap Ag | Efficient handling of multipart queries against relational data |
US20110125736A1 (en) * | 2005-09-30 | 2011-05-26 | Dave Kushal B | Selecting High Quality Reviews for Display |
US20070078669A1 (en) * | 2005-09-30 | 2007-04-05 | Dave Kushal B | Selecting representative reviews for display |
US20070124289A1 (en) * | 2005-11-30 | 2007-05-31 | Tomasz Imielinski | Remote execution of actions transparent to a user at registered remote entities in real-time |
WO2007076080A3 (en) * | 2005-12-22 | 2008-05-08 | Entrieva Inc | Analyzing content to determine context and serving relevant content based on the context |
US20070174255A1 (en) * | 2005-12-22 | 2007-07-26 | Entrieva, Inc. | Analyzing content to determine context and serving relevant content based on the context |
US20070156642A1 (en) * | 2005-12-29 | 2007-07-05 | Stoychev Mladen L | Database access method |
US7693819B2 (en) * | 2005-12-29 | 2010-04-06 | Sap Ag | Database access system and method for transferring portions of an ordered record set responsive to multiple requests |
US20070174266A1 (en) * | 2006-01-25 | 2007-07-26 | Gu Ta Internet Information Co., Ltd. | Method of optimization of listed result of internet-based search and system based on the method |
US7493403B2 (en) | 2006-03-13 | 2009-02-17 | Markmonitor Inc. | Domain name ownership validation |
US7475069B2 (en) * | 2006-03-29 | 2009-01-06 | International Business Machines Corporation | System and method for prioritizing websites during a webcrawling process |
US20080256046A1 (en) * | 2006-03-29 | 2008-10-16 | Blackman David L | System and method for prioritizing websites during a webcrawling process |
US7966337B2 (en) | 2006-03-29 | 2011-06-21 | International Business Machines Corporation | System and method for prioritizing websites during a webcrawling process |
KR101063364B1 (en) | 2006-03-29 | 2011-09-07 | 인터내셔널 비지네스 머신즈 코포레이션 | System and method for prioritizing websites during the web crawling process |
US20070239701A1 (en) * | 2006-03-29 | 2007-10-11 | International Business Machines Corporation | System and method for prioritizing websites during a webcrawling process |
US8990210B2 (en) * | 2006-03-31 | 2015-03-24 | Google Inc. | Propagating information among web pages |
US20140052735A1 (en) * | 2006-03-31 | 2014-02-20 | Daniel Egnor | Propagating Information Among Web Pages |
US20070280537A1 (en) * | 2006-06-05 | 2007-12-06 | Microsoft Corporation | Balancing out-of-dictionary and in-dictionary recognition scores |
US7899251B2 (en) * | 2006-06-05 | 2011-03-01 | Microsoft Corporation | Balancing out-of-dictionary and in-dictionary recognition scores |
US8555182B2 (en) | 2006-06-07 | 2013-10-08 | Microsoft Corporation | Interface for managing search term importance relationships |
US20070288498A1 (en) * | 2006-06-07 | 2007-12-13 | Microsoft Corporation | Interface for managing search term importance relationships |
US7849079B2 (en) | 2006-07-31 | 2010-12-07 | Microsoft Corporation | Temporal ranking of search results |
US7685199B2 (en) | 2006-07-31 | 2010-03-23 | Microsoft Corporation | Presenting information related to topics extracted from event classes |
US7577718B2 (en) | 2006-07-31 | 2009-08-18 | Microsoft Corporation | Adaptive dissemination of personalized and contextually relevant information |
US20080027979A1 (en) * | 2006-07-31 | 2008-01-31 | Microsoft Corporation | Presenting information related to topics extracted from event classes |
US20080027921A1 (en) * | 2006-07-31 | 2008-01-31 | Microsoft Corporation | Temporal ranking of search results |
US20080028036A1 (en) * | 2006-07-31 | 2008-01-31 | Microsoft Corporation | Adaptive dissemination of personalized and contextually relevant information |
US20100241352A1 (en) * | 2006-08-28 | 2010-09-23 | Assimakis Tzamaloukas | System and method for location-based searches and advertising |
US20080051048A1 (en) * | 2006-08-28 | 2008-02-28 | Assimakis Tzamaloukas | System and method for updating information using limited bandwidth |
US8280395B2 (en) | 2006-08-28 | 2012-10-02 | Dash Navigation, Inc. | System and method for updating information using limited bandwidth |
US8612437B2 (en) * | 2006-08-28 | 2013-12-17 | Blackberry Limited | System and method for location-based searches and advertising |
US20080052276A1 (en) * | 2006-08-28 | 2008-02-28 | Assimakis Tzamaloukas | System and method for location-based searches and advertising |
US20080059424A1 (en) * | 2006-08-28 | 2008-03-06 | Assimakis Tzamaloukas | System and method for locating-based searches and advertising |
US20080059283A1 (en) * | 2006-08-31 | 2008-03-06 | Ebay Inc. | Method and system for opportunity distribution |
US20080082528A1 (en) * | 2006-10-03 | 2008-04-03 | Pointer S.R.L. | Systems and methods for ranking search engine results |
US20080228675A1 (en) * | 2006-10-13 | 2008-09-18 | Move, Inc. | Multi-tiered cascading crawling system |
US20080104042A1 (en) * | 2006-10-25 | 2008-05-01 | Microsoft Corporation | Personalized Search Using Macros |
US20080104113A1 (en) * | 2006-10-26 | 2008-05-01 | Microsoft Corporation | Uniform resource locator scoring for targeted web crawling |
US7672943B2 (en) * | 2006-10-26 | 2010-03-02 | Microsoft Corporation | Calculating a downloading priority for the uniform resource locator in response to the domain density score, the anchor text score, the URL string score, the category need score, and the link proximity score for targeted web crawling |
US20080147710A1 (en) * | 2006-12-19 | 2008-06-19 | Microsoft Corporation | Generating web pages utilizing user-defined search parameters |
US20080154880A1 (en) * | 2006-12-26 | 2008-06-26 | Gu Ta Internet Information Co., Ltd. | Method of displaying listed result of internet-based search |
US8244708B2 (en) | 2007-02-01 | 2012-08-14 | John Nagle | System and method for improving integrity of internet search |
US8046346B2 (en) | 2007-02-01 | 2011-10-25 | John Nagle | System and method for improving integrity of internet search |
US20080189263A1 (en) * | 2007-02-01 | 2008-08-07 | John Nagle | System and method for improving integrity of internet search |
US7693833B2 (en) | 2007-02-01 | 2010-04-06 | John Nagle | System and method for improving integrity of internet search |
US20080249798A1 (en) * | 2007-04-04 | 2008-10-09 | Atul Tulshibagwale | Method and System of Ranking Web Content |
US7809714B1 (en) | 2007-04-30 | 2010-10-05 | Lawrence Richard Smith | Process for enhancing queries for information retrieval |
US8037042B2 (en) | 2007-05-10 | 2011-10-11 | Microsoft Corporation | Automated analysis of user search behavior |
US20080281809A1 (en) * | 2007-05-10 | 2008-11-13 | Microsoft Corporation | Automated analysis of user search behavior |
US20080281808A1 (en) * | 2007-05-10 | 2008-11-13 | Microsoft Corporation | Recommendation of related electronic assets based on user search behavior |
US7752201B2 (en) * | 2007-05-10 | 2010-07-06 | Microsoft Corporation | Recommendation of related electronic assets based on user search behavior |
US8756220B1 (en) | 2007-05-23 | 2014-06-17 | Google Inc. | Modifying search result ranking based on corpus search statistics |
US8359309B1 (en) | 2007-05-23 | 2013-01-22 | Google Inc. | Modifying search result ranking based on corpus search statistics |
US20080306830A1 (en) * | 2007-06-07 | 2008-12-11 | Cliquality, Llc | System for rating quality of online visitors |
US20080319975A1 (en) * | 2007-06-22 | 2008-12-25 | Microsoft Corporation | Exploratory Search Technique |
US8335719B1 (en) * | 2007-06-26 | 2012-12-18 | Amazon Technologies, Inc. | Generating advertisement sets based on keywords extracted from data feeds |
US20090006358A1 (en) * | 2007-06-27 | 2009-01-01 | Microsoft Corporation | Search results |
US20090138435A1 (en) * | 2007-11-26 | 2009-05-28 | Leslie Mannion | Techniques for searching and presenting search results |
US20090144266A1 (en) * | 2007-12-04 | 2009-06-04 | Eclipsys Corporation | Search method for entries in a database |
US9305113B2 (en) | 2007-12-07 | 2016-04-05 | Google Inc. | Contextual query revision |
US8626785B2 (en) * | 2007-12-07 | 2014-01-07 | Google Inc. | Contextual query revision |
US20110219441A1 (en) * | 2007-12-07 | 2011-09-08 | Google Inc. | Contextual Query Revision |
US8996554B2 (en) | 2007-12-07 | 2015-03-31 | Google Inc. | Contextual query revision |
US20090187571A1 (en) * | 2008-01-18 | 2009-07-23 | Treece Jeffrey C | Method Of Putting Items Into Categories According To Rank |
US8583645B2 (en) | 2008-01-18 | 2013-11-12 | International Business Machines Corporation | Putting items into categories according to rank |
US20100036830A1 (en) * | 2008-08-07 | 2010-02-11 | Yahoo! Inc. | Context based search arrangement for mobile devices |
US9367618B2 (en) | 2008-08-07 | 2016-06-14 | Yahoo! Inc. | Context based search arrangement for mobile devices |
US8700648B2 (en) * | 2009-03-16 | 2014-04-15 | Yahoo! | Context based networking |
US20100235389A1 (en) * | 2009-03-16 | 2010-09-16 | Yahoo! Inc. | Context based networking |
US20100293036A1 (en) * | 2009-05-15 | 2010-11-18 | France Telecom | Device and a method for updating a user profile |
US20100318425A1 (en) * | 2009-06-12 | 2010-12-16 | Meherzad Ratan Karanjia | System and method for providing a personalized shopping assistant for online computer users |
US20110093488A1 (en) * | 2009-10-15 | 2011-04-21 | Amacker Matthew W | Dynamic search suggestion and category specific completion |
US11080314B2 (en) | 2009-10-15 | 2021-08-03 | A9.Com, Inc. | Dynamic search suggestion and category specific completion |
US9405841B2 (en) * | 2009-10-15 | 2016-08-02 | A9.Com, Inc. | Dynamic search suggestion and category specific completion |
US20120290568A1 (en) * | 2009-12-29 | 2012-11-15 | Nhn Corporation | System and method for providing search results |
US9201951B2 (en) * | 2009-12-29 | 2015-12-01 | Nhn Corporation | System and method for providing search results |
US8661027B2 (en) | 2010-04-30 | 2014-02-25 | Alibaba Group Holding Limited | Vertical search-based query method, system and apparatus |
WO2011137125A1 (en) * | 2010-04-30 | 2011-11-03 | Alibaba Group Holding Limited | Vertical search-based query method, system and apparatus |
CN102236663A (en) * | 2010-04-30 | 2011-11-09 | 阿里巴巴集团控股有限公司 | Query method, query system and query device based on vertical search |
US9436764B2 (en) | 2010-06-29 | 2016-09-06 | Microsoft Technology Licensing, Llc | Navigation to popular search results |
US20150294004A1 (en) * | 2010-10-26 | 2015-10-15 | Google Inc. | Rich results relevant to user search queries for books |
US10509830B2 (en) * | 2010-10-26 | 2019-12-17 | Google Llc | Rich results relevant to user search queries |
US20180253498A1 (en) * | 2010-10-26 | 2018-09-06 | Google Llc | Rich results relevant to user search queries |
US9886517B2 (en) | 2010-12-07 | 2018-02-06 | Alibaba Group Holding Limited | Ranking product information |
WO2012078481A3 (en) * | 2010-12-07 | 2013-08-22 | Alibaba Group Holding Limited | Ranking product information |
US9400995B2 (en) | 2011-08-16 | 2016-07-26 | Alibaba Group Holding Limited | Recommending content information based on user behavior |
US20130080460A1 (en) * | 2011-09-22 | 2013-03-28 | Microsoft Corporation | Providing topic based search guidance |
US9043350B2 (en) * | 2011-09-22 | 2015-05-26 | Microsoft Technology Licensing, Llc | Providing topic based search guidance |
US8972844B2 (en) * | 2011-10-28 | 2015-03-03 | International Business Machines Corporation | Dynamic digital document fields behavior |
US9208437B2 (en) | 2011-12-16 | 2015-12-08 | Alibaba Group Holding Limited | Personalized information pushing method and device |
US8954570B2 (en) * | 2011-12-30 | 2015-02-10 | Brightedge Technologies, Inc. | System and method for estimating organic web traffic from a secured source |
US9202228B2 (en) | 2011-12-30 | 2015-12-01 | Brightedge Technologies, Inc. | System and method for estimating organic web traffic from a secured source |
US20130173783A1 (en) * | 2011-12-30 | 2013-07-04 | Brightedge Technologies, Inc. | System and method for estimating organic web traffic from a secured source |
US9116994B2 (en) * | 2012-01-09 | 2015-08-25 | Brightedge Technologies, Inc. | Search engine optimization for category specific search results |
US20130179420A1 (en) * | 2012-01-09 | 2013-07-11 | Brightedge Technologies, Inc. | Search engine optimization for category specific search results |
US8954463B2 (en) * | 2012-02-29 | 2015-02-10 | International Business Machines Corporation | Use of statistical language modeling for generating exploratory search results |
US8954466B2 (en) * | 2012-02-29 | 2015-02-10 | International Business Machines Corporation | Use of statistical language modeling for generating exploratory search results |
US20140289219A1 (en) * | 2012-04-05 | 2014-09-25 | Brightedge Technologies, Inc. | Ranking search engine results |
US9275153B2 (en) * | 2012-04-05 | 2016-03-01 | Brightedge Technologies, Inc. | Ranking search engine results |
US8768907B2 (en) | 2012-04-05 | 2014-07-01 | Brightedge Technologies, Inc. | Ranking search engine results |
US20130339003A1 (en) * | 2012-06-19 | 2013-12-19 | International Business Machines Corporation | Assisted Free Form Decision Definition Using Rules Vocabulary |
US10719663B2 (en) | 2012-06-19 | 2020-07-21 | International Business Machines Corporation | Assisted free form decision definition using rules vocabulary |
US10296585B2 (en) * | 2012-06-19 | 2019-05-21 | International Business Machines Corporation | Assisted free form decision definition using rules vocabulary |
US10261938B1 (en) | 2012-08-31 | 2019-04-16 | Amazon Technologies, Inc. | Content preloading using predictive models |
US9355140B1 (en) | 2013-03-13 | 2016-05-31 | Google Inc. | Associating an entity with a search query |
US11294970B1 (en) | 2013-03-13 | 2022-04-05 | Google Llc | Associating an entity with a search query |
US9336211B1 (en) | 2013-03-13 | 2016-05-10 | Google Inc. | Associating an entity with a search query |
US9870423B1 (en) | 2013-03-13 | 2018-01-16 | Google Llc | Associating an entity with a search query |
US10789309B1 (en) | 2013-03-13 | 2020-09-29 | Google Llc | Associating an entity with a search query |
RU2698405C2 (en) * | 2013-05-31 | 2019-08-26 | Ксилопикс | Method of search in database |
US9886694B2 (en) | 2013-09-27 | 2018-02-06 | Brightedge Technologies, Inc. | Secured search |
US20150095291A1 (en) * | 2013-09-30 | 2015-04-02 | Wal-Mart Stores, Inc. | Identifying Product Groups in Ecommerce |
US9633103B2 (en) * | 2013-09-30 | 2017-04-25 | Wal-Mart Stores, Inc. | Identifying product groups in ecommerce |
US11347758B2 (en) | 2014-12-23 | 2022-05-31 | Alibaba Group Holding Limited | Method and apparatus for processing search data |
US10635678B2 (en) | 2014-12-23 | 2020-04-28 | Alibaba Group Holding Limited | Method and apparatus for processing search data |
US10884580B2 (en) * | 2015-06-07 | 2021-01-05 | Apple Inc. | Devices and methods for displaying content in a note-taking application |
US11200498B2 (en) * | 2016-05-12 | 2021-12-14 | International Business Machines Corporation | Altering input search terms |
US11334800B2 (en) | 2016-05-12 | 2022-05-17 | International Business Machines Corporation | Altering input search terms |
US10261972B2 (en) * | 2016-09-07 | 2019-04-16 | IntelligenceNODE Consulting Private Limited | Methods and systems for similarity matching |
US20180067944A1 (en) * | 2016-09-07 | 2018-03-08 | IntelligenceNODE Consulting Private Limited | Methods and systems for similarity matching |
US10997249B2 (en) * | 2016-09-26 | 2021-05-04 | International Business Machines Corporation | Search query intent |
US10296659B2 (en) * | 2016-09-26 | 2019-05-21 | International Business Machines Corporation | Search query intent |
WO2019182859A1 (en) * | 2018-03-19 | 2019-09-26 | Walmart Apollo, Llc | Rule-based automated retail product line linkage |
US20190287145A1 (en) * | 2018-03-19 | 2019-09-19 | Walmart Apollo, Llc | Rule-based automated retail product line linkage |
CN110659388A (en) * | 2019-10-10 | 2020-01-07 | 北京奇艺世纪科技有限公司 | To-be-recommended information screening method and device, electronic equipment and storage medium |
Also Published As
Publication number | Publication date |
---|---|
US6963867B2 (en) | 2005-11-08 |
US20030195877A1 (en) | 2003-10-16 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US6963867B2 (en) | Search query processing to provide category-ranked presentation of search results | |
US7395259B2 (en) | Search engine system and associated content analysis methods for locating web pages with product offerings | |
EP1240605A1 (en) | System and method for locating and displaying web-based product offerings | |
US7424486B2 (en) | Selection of search phrases to suggest to users in view of actions performed by prior users | |
US20180239798A1 (en) | System to generate related search queries | |
US7412442B1 (en) | Augmenting search query results with behaviorally related items | |
US9443254B2 (en) | Product placement engine and method | |
US7603367B1 (en) | Method and system for displaying attributes of items organized in a searchable hierarchical structure | |
US8117072B2 (en) | Promoting strategic documents by bias ranking of search results on a web browser | |
CN100568232C (en) | the universal search engine interface | |
US20040078214A1 (en) | Product recommendation in a network-based commerce system | |
US6125395A (en) | Method for identifying collections of internet web sites with domain names | |
US8051059B2 (en) | Supplier identification and locator system and method | |
US20010044837A1 (en) | Methods and systems for searching an information directory | |
US20110137913A1 (en) | Systems and Methods for Interest-Driven Stock market Segmentation and Stock Trading | |
JP2007293896A (en) | System and method for refining search queries | |
WO2001042880A2 (en) | Search query refinement using related search phrases | |
WO2001013273A2 (en) | Systems, methods and computer program products for performing multi-lingual, multi-cultural searches, comparisons, and purchases of products offered for sale at multiple web sites on the internet | |
dos Santos et al. | Building comparison-shopping brokers on the web | |
CN115640436A (en) | Information display method | |
AU2015204354A1 (en) | System to generate related search queries | |
AU2013203878A1 (en) | System to generate related search queries |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
STCB | Information on status: application discontinuation |
Free format text: ABANDONED -- AFTER EXAMINER'S ANSWER OR BOARD OF APPEALS DECISION |