US20130132824A1 - System and method for context and community based customization for a user experience - Google Patents

System and method for context and community based customization for a user experience Download PDF

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US20130132824A1
US20130132824A1 US13/746,184 US201313746184A US2013132824A1 US 20130132824 A1 US20130132824 A1 US 20130132824A1 US 201313746184 A US201313746184 A US 201313746184A US 2013132824 A1 US2013132824 A1 US 2013132824A1
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Prior art keywords
user
user experience
customizer
context information
information
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US13/746,184
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Vipul C. Dalal
Eric Noel Billingsley
James Ladd
Sanjay Pundlkrao Ghatare
Randall Scott Shoup
Suhail Ansari
Gunshekar Cemballi
Neelakantan Sundaresan
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PayPal Inc
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eBay Inc
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Priority to US13/746,184 priority Critical patent/US20130132824A1/en
Assigned to EBAY INC. reassignment EBAY INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: ANSARI, SUHAIL, SHOUP, RANDALL S., GHATARE, SANJAY P., BILLINGSLEY, ERIC N., DALAL, VIPUL C., LADD, JAMES, CEMBALLI, GUNSHEKAR, SUNDARESAN, NEELAKANTAN
Publication of US20130132824A1 publication Critical patent/US20130132824A1/en
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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/048Interaction techniques based on graphical user interfaces [GUI]
    • G06F3/0484Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION 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
    • G06Q30/00Commerce
    • G06Q30/02Marketing; Price estimation or determination; Fundraising
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION 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
    • G06Q30/00Commerce
    • G06Q30/02Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201Market modelling; Market analysis; Collecting market data
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION 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
    • G06Q30/00Commerce
    • G06Q30/02Marketing; Price estimation or determination; Fundraising
    • G06Q30/0241Advertisements
    • G06Q30/0251Targeted advertisements
    • G06Q30/0255Targeted advertisements based on user history
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION 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
    • G06Q30/00Commerce
    • G06Q30/06Buying, selling or leasing transactions
    • G06Q30/0601Electronic shopping [e-shopping]
    • G06Q30/0603Catalogue ordering

Definitions

  • This disclosure relates to methods and systems supporting computing and data processing systems. More particularly, a system and method for context and community based customization for a user experience is described.
  • U.S. Pat. No. 6,981,040 describes a method for providing automatic, personalized information services to a computer user including the following steps: transparently monitoring user interactions with data during normal use of the computer; updating user-specific data files including a set of user-related documents; estimating parameters of a learning machine that define a User Model specific to the user, using the user-specific data files; analyzing a document to identify its properties; estimating the probability that the user is interested in the document by applying the document properties to the parameters of the User Model; and providing personalized services based on the estimated probability.
  • Personalized services include personalized searches that return only documents of interest to the user, personalized crawling for maintaining an index of documents of interest to the user; and personalized navigation that recommends interesting documents that are hyperlinked to documents currently being viewed.
  • U.S. Published Patent Application No. 2007/0100867 describes a method for providing advertising content for display in a page over a network.
  • a plurality of advertisements are determined that are qualified for display at a location in the page.
  • an advertiser has stores located at a plurality of geographic sites, only one advertisement for a store located at a first geographic site may be displayed. Thereafter, the advertisement for a store located at a second geographic site different from the first geographic site may be displayed.
  • U.S. Published Patent Application No. 2007/0208724 describes a system and method to facilitate expansion, disambiguation, and optimization of search queries over a network wherein an original query received from a user is parsed to obtain at least one query term. A plurality of keywords related contextually to one or more query terms are further retrieved from a database. Finally, a set of modified queries is generated, each modified query further comprising at least one query term and at least one retrieved keyword.
  • FIG. 1 illustrates an example of a particular embodiment of the automated, community-driven, self-learning system.
  • FIG. 2 illustrates the user experience customizer of a particular embodiment.
  • FIG. 3 illustrates the multiple input dimensions and multiple output dimensions of a particular embodiment.
  • FIG. 4 illustrates a processing flow diagram for an example embodiment.
  • FIG. 5 shows a diagrammatic representation of a machine in the form of a computer system within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed, according to an example embodiment.
  • the user experience includes a computer-implemented user interface and functionality supporting the processing capabilities provided for a computer user.
  • Various embodiments are described below in connection with the figures provided herein.
  • an automated, community-driven, self-learning system uses collected user activity feedback to customize the serving of web page content to users in a context-sensitive manner.
  • the system uses context input, including the user's search query/keywords, a related product or service category, a user/segment profile, site identifier (ID), domain, etc., and user activity feedback to perform the following customization operations:
  • the automated, community-driven, self-learning system uses multi-dimensional input (context input) to produce multi-dimensional output (selections of page type, widget set, and/or configuration) all based on (customized from) a collection of activity feedback from a community of users gathered over time.
  • a widget or module is an interface element with which a computer user interacts, such as a window, frame, or a text box.
  • the defining characteristic of a widget is to provide a single interaction point for the direct manipulation of a given kind of data.
  • Widgets are visual basic building blocks which, when combined in an application, hold all the data processed by the application and the available interactions on this data.
  • various embodiments use context input, including user and query information and user activity feedback to automatically generate and display the most relevant or most likely user-favored next page for that context using a predictive model.
  • User information can include explicitly or implicitly obtained demographic information, explicitly or implicitly obtained user profile information, user transaction history, user activity history, and/or any other information explicitly or implicitly obtained that may indicate user preferences.
  • a perturbation engine is used to include, for some users, a slightly sub-optimal selection of page type, widget set, and/or configuration to cause the system to re-affirm the optimal selections and to introduce new selections that may have otherwise not been considered or selected.
  • the perturbation engine enables a particular user or set of users to be exposed to a selection of page type, widget set, and/or configuration to which the user/users may not have otherwise been exposed. In some cases, a particular user or set of users can be exposed to a sub-optimal or under-performing selection of page type, widget set, and/or configuration.
  • FIG. 1 illustrates an example of a particular embodiment of the automated, community-driven, self-learning system.
  • networked computer users can use various servers (e.g. websites available via a public network such as the Internet) and search engines to perform various operations, such as searching for items using search queries and a search engine, performing e-commerce transactions, shopping or bidding on goods or services, browsing for information or items of interest, and the like.
  • these user operations include some form of user input (e.g. a search query or set of keywords entered as text in an input field of a search engine).
  • This user input provides one form of context input used by user experience customizer 100 to automatically customize the user experience for the user community.
  • context input collected and/or used by the user experience customizer 100 can include, a related product or service category, a user/segment profile or other user information, site identifier (ID), domain, etc.
  • the related product or service category can include category(s) of products or services that relate to the searches or e-commerce transactions a user may have currently or previously submitted.
  • a user/segment profile or other user information represents a user profile explicitly entered by a user or implicitly generated based on past user activity or behavior.
  • the user profile can specify various demographic information, configurations, defaults, preferences, and the like associated with a particular user or group of users.
  • User information can include explicitly or implicitly obtained demographic information, explicitly or implicitly obtained user profile or preference information, user transaction history, user activity history, and/or any other information explicitly or implicitly obtained that may indicate user preferences.
  • the site identifier (ID) or domain name can specify a particular network location or geographic location associated with a user or group of users. It will be apparent to those of ordinary skill in the art that other information can be retrieved as context information or input associated with a particular point in time.
  • this context input can be provided to user experience customizer 100 .
  • user experience customizer 100 includes predictive data and associated computer-implemented rules that can be applied to the context input to produce decisions or selections related to the type of user experience to present to the user that will represent the most relevant or most likely favored user experience for the user based on the context input.
  • a user experience, including user interface and available functionality in the form of a webpage 110 can be generated by user experience customizer 100 .
  • This webpage 110 can include a particular page type selected by the user experience customizer 100 from a plurality of available page types described above (e.g.
  • the page type can define the structure and/or arrangement of information and images provided on the webpage.
  • a plurality of modules or widgets 112 can be placed in the available locations of the selected page type.
  • the particular modules placed in page 110 are selected by the user experience customizer 100 from a plurality of available page modules or widgets (e.g. list, graphic, data input, etc.).
  • the information content thr each of the modules 112 is selected by the user experience customizer 100 from a plurality of available information content sources 111 (e.g. store locations, merchandise listings, advertising items, etc.).
  • the predictive model can further configure the information content displayed in modules 112 based on the context input.
  • the particular configuration of information content displayed in modules 112 of page 110 is selected by the user experience customizer 100 from a plurality of available information content configurations (e.g. sort order, list or gallery display, expansion display, etc.).
  • context input 105 is provided to user experience customizer 100 .
  • the user experience customizer 100 of a particular embodiment is shown to include an input unit 211 to receive the context input 105 from the various sources described above.
  • the processed context input is provided to predictive data unit 212 .
  • Predictive data unit 212 can take the processed context data and form correlations between the context data and the likely desirable structure and content provided in a corresponding user experience. These correlations can be resolved into decisions or selections made by the decision unit 213 based on the correlations made by the predictive data unit 212 .
  • the selections made by decision unit 213 include a selection of page type for the output page 110 , a selection of modules 112 for the selected output page type 110 , and a selection of configuration of content 114 displayed in the selected modules 112 of output page 110 .
  • the system of a particular embodiment shown in FIG. 2 can collect user activity feedback from a community of users 220 who interact with the output page 110 .
  • networked computer users can use various servers (e.g. websites available via a public network such as the Internet) to perform various operations on user interfaces (e.g. web pages, including output page 110 ), such as searching for items using search queries and a search engine, performing e-commerce transactions, shopping or bidding on goods or services, browsing for information or items of interest, and the like.
  • These user-performed operations include various activities performed by the users, such as using a pointing device (e.g.
  • the user activity feedback 222 is collected from the user community 220 by a user activity feedback aggregation unit 224 .
  • the user activity feedback aggregation unit 224 produces structured and processed user activity feedback that can be used by the user experience customizer 100 to adjust the predictive data unit 212 .
  • the rules implemented in predictive data unit 212 can be biased or weighted to produce selections that are more likely favored by the user community based on the user activity feedback 222 .
  • user experience customizer 100 can also include a separate customizer for each of a plurality of region/sites as provided in tabs 235 .
  • Regions can include, for example, countries, states, geographical regions, and the like.
  • Sites can include areas served by one or more computing sites, hubs, servers or server farms, and the like. Given a region/sites tab selection, the user experience customizer 100 can be configured to produce a different set of customized user interface pages 110 and different associated functionality that are specifically customized for a selected region/site and based on user activity feedback that is relevant for that selected region/site.
  • FIG. 2 also illustrates that the system of a particular embodiment can include an administrator access/control level that is accessed via an administration console 230 .
  • the administrator can cause the generation and display of various reports 232 that highlight the internal operation of the user experience customizer 100 .
  • the administration console 230 provides a view into how the user experience customizer 100 has made decisions over time. For example, the administration console 230 provides a view into how a decision was made to promote/demote a particular page type, module type, or configuration fora particular set of context input.
  • the context input provided to user experience customizer 100 can include multiple dimensions including, for example, site 251 , buyer segmentation 252 , domain 253 , keywords or search query 254 , and other context related data 255 .
  • Site 25 information can include a user/buyer name, location, community code, IP address, user profile, and the like.
  • Buyer segmentation 252 can include information that classifies the user/buyer into one or more purchaser/bidder/shopper groups based on pre-determined criteria.
  • Domain 253 can include information identifying the server, website, merchant, or location, which the user/buyer has accessed. Keywords or search query 254 represents the user query 105 entered by a user.
  • the items or dimensions included in the context information can be dynamically prioritized, re-ordered, and/or re-grouped so the user experience customizer 100 can receive the best context input available in a given situation. For example, if a particular item or dimension included in the context information does not provide sufficient or accurate information related to the particular dimension, the insufficient or inaccurate dimension can be re-ordered to a less valued position in the group of context information or the dimension can be eliminated from the context information altogether. In this manner, items or dimensions included in the context information can be ordered or grouped to fall back progressively to other sufficient and accurate dimensions in the group if a particular dimension does not provide sufficient or accurate information for the user experience customizer 100 .
  • the output produced by the user experience customizer 100 can include multi-dimensional output, such as selections of page type 261 , module/widget set 262 , configuration 263 , and/or other selections 264 ) all based on (customized from) a collection of user activity feedback from a community of users gathered over time.
  • various embodiments use context input, including user and query information and user activity feedback to automatically generate and display the most relevant next page for that context using a predictive model.
  • FIG. 4 illustrates a processing flow diagram for an example embodiment.
  • the system gathers context information (processing block 471 ); provides a user experience customizer to automatically produce user experience customization selections based on the context information (processing block 472 ); collects user activity feedback from a community of users (processing block 473 ); and uses the user activity feedback to adjust the user experience customizer to automatically produce user experience customization selections likely favored by a user based on a correlation of the user activity feedback with the context information (processing block 474 ).
  • FIG. 5 shows a diagrammatic representation of a machine in the example form of a computer system 700 within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed.
  • the machine operates as a standalone device or may be connected (e.g., networked) to other machines.
  • the machine may operate in the capacity of a server or a client machine in client-server network environment, or as a peer machine in a peer-to-peer (or distributed) network environment.
  • the machine may be a server computer, a client computer, a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine.
  • PC personal computer
  • PDA Personal Digital Assistant
  • STB set-top box
  • a cellular telephone a web appliance
  • network router switch or bridge
  • the example computer system 700 includes a processor 702 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), or both), a main memory 704 and a static memory 706 , which communicate with each other via a bus 708 .
  • the computer system 700 may further include a video display unit 710 (e.g. a liquid crystal display (LCD) or a cathode ray tube (CRT)).
  • the computer system 700 also includes an input device 712 (e.g., a keyboard), a cursor control device 714 (e.g., a mouse), a disk drive unit 716 , a signal generation device 718 (e.g., a speaker) and a network interface device 720 .
  • the disk drive unit 716 includes a machine-readable medium 722 on which is stored one or more sets of instructions (e.g., software 724 ) embodying any one or more of the methodologies or functions described herein.
  • the instructions 724 may also reside, completely or at least partially, within the main memory 704 , the static memory 706 , and/or within the processor 702 during execution thereof by the computer system 700 .
  • the main memory 704 and the processor 702 also may constitute machine-readable media.
  • the instructions 724 may further be transmitted or received over a network 726 via the network interface device 720 .
  • a computer system e.g., a standalone, client or server computer system
  • an application may constitute a “module” that is configured and operates to perform certain operations as described herein.
  • the “module” may be implemented mechanically or electronically.
  • a module may comprise dedicated circuitry or logic that is permanently configured (e.g., within a special-purpose processor) to perform certain operations.
  • a module may also comprise programmable logic or circuitry (e.g., as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations. It will be appreciated that the decision to implement a module mechanically, in the dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g. configured by software) may be driven by cost and time considerations. Accordingly, the term “module” should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired) or temporarily configured (e.g., programmed) to operate in a certain manner and/or to perform certain operations described herein.
  • machine-readable medium 722 is shown in an example embodiment to be a single medium, the term “machine-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions.
  • the term “machine-readable medium” shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present description.
  • the term “machine-readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical and magnetic media, and carrier wave signals.
  • the software may be transmitted over a network using a transmission medium.
  • the term “transmission medium” shall be taken to include any medium that is capable of storing, encoding or carrying instructions for transmission to and execution by the machine, and includes digital or analog communications signal or other intangible medium to facilitate transmission and communication of such software.

Abstract

A system and method for context and community based customization for a user experience is disclosed. The apparatus in an example embodiment includes a user experience customizer to gather context information, automatically produce user experience customization selections based on the context information, collect user activity feedback from a community of users, and use the user activity feedback to adjust the user experience customizer to automatically produce user experience customization selections likely favored by a user based on a correlation of the user activity feedback with the context information.

Description

    RELATED APPLICATION
  • This application is a Continuation of U.S. Non-Provisional patent application Ser. No. 12/126,309, filed May 23, 2008, which application is incorporated herein by reference in its entirety.
  • COPYRIGHT NOTICE
  • A portion of the disclosure of this patent document contains material that is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all copyright rights whatsoever. The following notice applies to the software and data as described below and in the drawings that form a part of this document: Copyright 2007-2008, eBay Inc., All Rights Reserved.
  • BACKGROUND
  • 1. Technical Field
  • This disclosure relates to methods and systems supporting computing and data processing systems. More particularly, a system and method for context and community based customization for a user experience is described.
  • 2. Related Art
  • Conventional systems, like Amazon.com, can use a buyer's previously purchased product or product category/genre to suggest new products in a same or similar category/genre for the user. However, these prior systems are typically one-dimensional. That is, one-dimensional input (e.g. product category/genre) leads to one-dimensional output (e.g. new products in a same or similar category/genre). These conventional systems cannot provide multi-dimensional context analysis to provide a multi-dimensional output based on (customized from) a collection of activity from a community of users gathered over time.
  • U.S. Pat. No. 6,981,040 describes a method for providing automatic, personalized information services to a computer user including the following steps: transparently monitoring user interactions with data during normal use of the computer; updating user-specific data files including a set of user-related documents; estimating parameters of a learning machine that define a User Model specific to the user, using the user-specific data files; analyzing a document to identify its properties; estimating the probability that the user is interested in the document by applying the document properties to the parameters of the User Model; and providing personalized services based on the estimated probability. Personalized services include personalized searches that return only documents of interest to the user, personalized crawling for maintaining an index of documents of interest to the user; and personalized navigation that recommends interesting documents that are hyperlinked to documents currently being viewed.
  • U.S. Published Patent Application No. 2007/0100867 describes a method for providing advertising content for display in a page over a network. A plurality of advertisements are determined that are qualified for display at a location in the page. When an advertiser has stores located at a plurality of geographic sites, only one advertisement for a store located at a first geographic site may be displayed. Thereafter, the advertisement for a store located at a second geographic site different from the first geographic site may be displayed.
  • U.S. Published Patent Application No. 2007/0208724 describes a system and method to facilitate expansion, disambiguation, and optimization of search queries over a network wherein an original query received from a user is parsed to obtain at least one query term. A plurality of keywords related contextually to one or more query terms are further retrieved from a database. Finally, a set of modified queries is generated, each modified query further comprising at least one query term and at least one retrieved keyword.
  • Thus, a system and method for context and community based customization for a user experience are needed.
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • Embodiments illustrated by way of example and not limitation in the figures of the accompanying drawings, in which:
  • FIG. 1 illustrates an example of a particular embodiment of the automated, community-driven, self-learning system.
  • FIG. 2 illustrates the user experience customizer of a particular embodiment.
  • FIG. 3 illustrates the multiple input dimensions and multiple output dimensions of a particular embodiment.
  • FIG. 4 illustrates a processing flow diagram for an example embodiment.
  • FIG. 5 shows a diagrammatic representation of a machine in the form of a computer system within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed, according to an example embodiment.
  • DETAILED DESCRIPTION
  • In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of some example embodiments. It will be evident, however, to one of ordinary skill in the art that the present invention may be practiced without these specific details.
  • As described further below, according to various example embodiments of the disclosed subject matter described and claimed herein, there is provided a system and method for context and community based customization for a user experience. The user experience includes a computer-implemented user interface and functionality supporting the processing capabilities provided for a computer user. Various embodiments are described below in connection with the figures provided herein.
  • In an example embodiment, an automated, community-driven, self-learning system uses collected user activity feedback to customize the serving of web page content to users in a context-sensitive manner. The system uses context input, including the user's search query/keywords, a related product or service category, a user/segment profile, site identifier (ID), domain, etc., and user activity feedback to perform the following customization operations:
      • 1. choose among a variety of page types given the context input. The page types can include any type of webpage, window, frame, dialog box, user interface screen, textual or image display, or the like. Particular examples of such page types include, an all matching items (AMI) type, a dynamic landing page (DLP) type, a registration page, etc. It will be apparent to those of ordinary skill in the art the other page types can be similarly defined;
      • 2. on the selected page type, populate a likely relevant set of widgets/modules (e.g. advertisements, links, selection lists, information blocks, etc.) for display on the page given the context input; and
      • 3. for one or more widgets/modules, set a configuration for the widgets/modules (e.g. a sorting of the data) given the context input.
  • In various embodiments described herein, the automated, community-driven, self-learning system uses multi-dimensional input (context input) to produce multi-dimensional output (selections of page type, widget set, and/or configuration) all based on (customized from) a collection of activity feedback from a community of users gathered over time. As described herein, a widget (or module) is an interface element with which a computer user interacts, such as a window, frame, or a text box. The defining characteristic of a widget is to provide a single interaction point for the direct manipulation of a given kind of data. Widgets are visual basic building blocks which, when combined in an application, hold all the data processed by the application and the available interactions on this data.
  • In general, various embodiments use context input, including user and query information and user activity feedback to automatically generate and display the most relevant or most likely user-favored next page for that context using a predictive model. User information can include explicitly or implicitly obtained demographic information, explicitly or implicitly obtained user profile information, user transaction history, user activity history, and/or any other information explicitly or implicitly obtained that may indicate user preferences. Additionally, a perturbation engine is used to include, for some users, a slightly sub-optimal selection of page type, widget set, and/or configuration to cause the system to re-affirm the optimal selections and to introduce new selections that may have otherwise not been considered or selected. The perturbation engine enables a particular user or set of users to be exposed to a selection of page type, widget set, and/or configuration to which the user/users may not have otherwise been exposed. In some cases, a particular user or set of users can be exposed to a sub-optimal or under-performing selection of page type, widget set, and/or configuration.
  • FIG. 1 illustrates an example of a particular embodiment of the automated, community-driven, self-learning system. In a community of users 105, networked computer users can use various servers (e.g. websites available via a public network such as the Internet) and search engines to perform various operations, such as searching for items using search queries and a search engine, performing e-commerce transactions, shopping or bidding on goods or services, browsing for information or items of interest, and the like. Typically, these user operations include some form of user input (e.g. a search query or set of keywords entered as text in an input field of a search engine). This user input provides one form of context input used by user experience customizer 100 to automatically customize the user experience for the user community. Other forms of context input collected and/or used by the user experience customizer 100 can include, a related product or service category, a user/segment profile or other user information, site identifier (ID), domain, etc. The related product or service category can include category(s) of products or services that relate to the searches or e-commerce transactions a user may have currently or previously submitted. A user/segment profile or other user information represents a user profile explicitly entered by a user or implicitly generated based on past user activity or behavior. The user profile can specify various demographic information, configurations, defaults, preferences, and the like associated with a particular user or group of users. User information can include explicitly or implicitly obtained demographic information, explicitly or implicitly obtained user profile or preference information, user transaction history, user activity history, and/or any other information explicitly or implicitly obtained that may indicate user preferences. The site identifier (ID) or domain name can specify a particular network location or geographic location associated with a user or group of users. It will be apparent to those of ordinary skill in the art that other information can be retrieved as context information or input associated with a particular point in time.
  • As shown in FIG. 1, this context input can be provided to user experience customizer 100. As will be described in more detail below, user experience customizer 100 includes predictive data and associated computer-implemented rules that can be applied to the context input to produce decisions or selections related to the type of user experience to present to the user that will represent the most relevant or most likely favored user experience for the user based on the context input. As a result, in a particular embodiment, a user experience, including user interface and available functionality in the form of a webpage 110 can be generated by user experience customizer 100. This webpage 110 can include a particular page type selected by the user experience customizer 100 from a plurality of available page types described above (e.g. AMI—all matching items, DLP—dynamic landing page, VIP—view item page, etc.). The page type can define the structure and/or arrangement of information and images provided on the webpage. Based on the selected page type, a plurality of modules or widgets 112 can be placed in the available locations of the selected page type. The particular modules placed in page 110 are selected by the user experience customizer 100 from a plurality of available page modules or widgets (e.g. list, graphic, data input, etc.). Once the selected modules 112 are placed in the page 110, the information content thr each of the modules 112 is selected by the user experience customizer 100 from a plurality of available information content sources 111 (e.g. store locations, merchandise listings, advertising items, etc.). Once the content from the selected content sources are placed in the corresponding selected modules 112, the predictive model can further configure the information content displayed in modules 112 based on the context input. The particular configuration of information content displayed in modules 112 of page 110 is selected by the user experience customizer 100 from a plurality of available information content configurations (e.g. sort order, list or gallery display, expansion display, etc.).
  • Referring now to FIG. 2, a more detailed system view of a particular embodiment is shown. As described above, context input 105 is provided to user experience customizer 100. The user experience customizer 100 of a particular embodiment is shown to include an input unit 211 to receive the context input 105 from the various sources described above. Once the context input is collected, aggregated, filtered, and structured by input unit 211, the processed context input is provided to predictive data unit 212. Predictive data unit 212 can take the processed context data and form correlations between the context data and the likely desirable structure and content provided in a corresponding user experience. These correlations can be resolved into decisions or selections made by the decision unit 213 based on the correlations made by the predictive data unit 212. The selections made by decision unit 213 include a selection of page type for the output page 110, a selection of modules 112 for the selected output page type 110, and a selection of configuration of content 114 displayed in the selected modules 112 of output page 110.
  • Once the user experience customizer 100 produces and displays output page 110, the system of a particular embodiment shown in FIG. 2 can collect user activity feedback from a community of users 220 who interact with the output page 110. In the community of users 220, networked computer users can use various servers (e.g. websites available via a public network such as the Internet) to perform various operations on user interfaces (e.g. web pages, including output page 110), such as searching for items using search queries and a search engine, performing e-commerce transactions, shopping or bidding on goods or services, browsing for information or items of interest, and the like. These user-performed operations include various activities performed by the users, such as using a pointing device (e.g. computer mouse) to select, click, or mouseover various options, items, or links on a webpage, enter a search query or set of keywords, update a user profile, enter text into a user interface provided data entry field, browsing, shopping, bidding, or buying on-line, providing explicit feedback on a user experience, and other types of well-known user interactions with a computer-implemented user interface. These user activities can be recorded and saved in combination with information indicative of the structure and content of the webpage or user interface (e.g. page 110) with which the user was interacting at the time the user activity was recorded. This retained user activity feedback can be used to correlate the user's actions with the user interface acted upon. In this manner, user relevance or user desires is inferred from the user activity feedback. The use of this user activity feedback wilt be described in more detail below in connection with a particular embodiment.
  • As shown in FIG. 2, the user activity feedback 222 is collected from the user community 220 by a user activity feedback aggregation unit 224. The user activity feedback aggregation unit 224 produces structured and processed user activity feedback that can be used by the user experience customizer 100 to adjust the predictive data unit 212. For example, the rules implemented in predictive data unit 212 can be biased or weighted to produce selections that are more likely favored by the user community based on the user activity feedback 222.
  • As also shown in FIG. 2, user experience customizer 100 can also include a separate customizer for each of a plurality of region/sites as provided in tabs 235. Regions can include, for example, countries, states, geographical regions, and the like. Sites can include areas served by one or more computing sites, hubs, servers or server farms, and the like. Given a region/sites tab selection, the user experience customizer 100 can be configured to produce a different set of customized user interface pages 110 and different associated functionality that are specifically customized for a selected region/site and based on user activity feedback that is relevant for that selected region/site.
  • FIG. 2 also illustrates that the system of a particular embodiment can include an administrator access/control level that is accessed via an administration console 230. The administrator can cause the generation and display of various reports 232 that highlight the internal operation of the user experience customizer 100. The administration console 230 provides a view into how the user experience customizer 100 has made decisions over time. For example, the administration console 230 provides a view into how a decision was made to promote/demote a particular page type, module type, or configuration fora particular set of context input.
  • Referring now to FIG. 3, the multiple input dimensions and multiple output dimensions of a particular embodiment are shown. The context input provided to user experience customizer 100 can include multiple dimensions including, for example, site 251, buyer segmentation 252, domain 253, keywords or search query 254, and other context related data 255. Site 25 information can include a user/buyer name, location, community code, IP address, user profile, and the like. Buyer segmentation 252 can include information that classifies the user/buyer into one or more purchaser/bidder/shopper groups based on pre-determined criteria. Domain 253 can include information identifying the server, website, merchant, or location, which the user/buyer has accessed. Keywords or search query 254 represents the user query 105 entered by a user. The items or dimensions included in the context information can be dynamically prioritized, re-ordered, and/or re-grouped so the user experience customizer 100 can receive the best context input available in a given situation. For example, if a particular item or dimension included in the context information does not provide sufficient or accurate information related to the particular dimension, the insufficient or inaccurate dimension can be re-ordered to a less valued position in the group of context information or the dimension can be eliminated from the context information altogether. In this manner, items or dimensions included in the context information can be ordered or grouped to fall back progressively to other sufficient and accurate dimensions in the group if a particular dimension does not provide sufficient or accurate information for the user experience customizer 100.
  • The output produced by the user experience customizer 100 can include multi-dimensional output, such as selections of page type 261, module/widget set 262, configuration 263, and/or other selections 264) all based on (customized from) a collection of user activity feedback from a community of users gathered over time. In general, various embodiments use context input, including user and query information and user activity feedback to automatically generate and display the most relevant next page for that context using a predictive model.
  • FIG. 4 illustrates a processing flow diagram for an example embodiment. In the embodiment shown, the system gathers context information (processing block 471); provides a user experience customizer to automatically produce user experience customization selections based on the context information (processing block 472); collects user activity feedback from a community of users (processing block 473); and uses the user activity feedback to adjust the user experience customizer to automatically produce user experience customization selections likely favored by a user based on a correlation of the user activity feedback with the context information (processing block 474).
  • FIG. 5 shows a diagrammatic representation of a machine in the example form of a computer system 700 within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed. In alternative embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine may operate in the capacity of a server or a client machine in client-server network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a server computer, a client computer, a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.
  • The example computer system 700 includes a processor 702 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), or both), a main memory 704 and a static memory 706, which communicate with each other via a bus 708. The computer system 700 may further include a video display unit 710 (e.g. a liquid crystal display (LCD) or a cathode ray tube (CRT)). The computer system 700 also includes an input device 712 (e.g., a keyboard), a cursor control device 714 (e.g., a mouse), a disk drive unit 716, a signal generation device 718 (e.g., a speaker) and a network interface device 720.
  • The disk drive unit 716 includes a machine-readable medium 722 on which is stored one or more sets of instructions (e.g., software 724) embodying any one or more of the methodologies or functions described herein. The instructions 724 may also reside, completely or at least partially, within the main memory 704, the static memory 706, and/or within the processor 702 during execution thereof by the computer system 700. The main memory 704 and the processor 702 also may constitute machine-readable media. The instructions 724 may further be transmitted or received over a network 726 via the network interface device 720.
  • Applications that may include the apparatus and systems of various embodiments broadly include a variety of electronic and computer systems. Some embodiments implement functions in two or more specific interconnected hardware modules or devices with related control and data signals communicated between and through the modules, or as portions of an application-specific integrated circuit. Thus, the example system is applicable to software, firmware, and hardware implementations. In example embodiments, a computer system (e.g., a standalone, client or server computer system) configured by an application may constitute a “module” that is configured and operates to perform certain operations as described herein. In other embodiments, the “module” may be implemented mechanically or electronically. For example, a module may comprise dedicated circuitry or logic that is permanently configured (e.g., within a special-purpose processor) to perform certain operations. A module may also comprise programmable logic or circuitry (e.g., as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations. It will be appreciated that the decision to implement a module mechanically, in the dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g. configured by software) may be driven by cost and time considerations. Accordingly, the term “module” should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired) or temporarily configured (e.g., programmed) to operate in a certain manner and/or to perform certain operations described herein. While the machine-readable medium 722 is shown in an example embodiment to be a single medium, the term “machine-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “machine-readable medium” shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present description. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical and magnetic media, and carrier wave signals. As noted, the software may be transmitted over a network using a transmission medium. The term “transmission medium” shall be taken to include any medium that is capable of storing, encoding or carrying instructions for transmission to and execution by the machine, and includes digital or analog communications signal or other intangible medium to facilitate transmission and communication of such software.
  • The illustrations of embodiments described herein are intended to provide a general understanding of the structure of various embodiments, and they are not intended to serve as a complete description of all the elements and features of apparatus and systems that might make use of the structures described herein. Many other embodiments will be apparent to those of ordinary skill in the art upon reviewing the above description. Other embodiments may be utilized and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. The figures provided herein are merely representational and may not be drawn to scale. Certain proportions thereof may be exaggerated, while others may be minimized. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense.
  • Thus, a system and method for context and community based customization for a user experience are disclosed. While the present invention has been described in terms of several example embodiments, those of ordinary skill in the art will recognize that the present invention is not limited to the embodiments described, but can be practiced with modification and alteration within the spirit and scope of the appended claims. The description herein is thus to be regarded as illustrative instead of limiting.

Claims (26)

What is claimed is:
1. A method comprising:
gathering context information;
providing a user experience customizer to automatically produce user experience customization selections based on the context information;
collecting user activity feedback from a community of users; and
using the user activity feedback to adjust the user experience customizer to automatically produce user experience customization selections likely favored by a user based on a correlation of the user activity feedback with the context information.
2. The method as claimed in claim 1 wherein the context information is selected from the group: site identifier, buyer segmentation information, domain identifier, and keywords/queries.
3. The method as claimed in claim 1 wherein the context information is selected from dimensions in the group: site identifier, buyer segmentation information, domain identifier, and keywords/queries, and the method further includes progressively falling back to other sufficient and accurate dimensions in the group if a particular dimension does not provide sufficient or accurate information.
4. The method as claimed in claim 1 wherein the user experience customization selections are selected from the group: page type, modules, and configurations.
5. The method as claimed in claim 4 wherein the page type includes an all matching items page type.
6. The method as claimed in claim 4 wherein the configurations include a sort order selection.
7. The method as claimed in claim 1 including providing a perturbation engine to perturb the automatically produced user experience customization selections.
8. The method as claimed in claim 7 wherein the perturbation engine exposes a user to user experience customization selections to which the user would not have otherwise been exposed.
9. The method as claimed in claim 7 wherein the perturbation engine exposes a user to sub-optimal user experience customization selections.
10. The method as claimed in claim 1 wherein the user activity feedback includes clicking on items, bidding or buying activity, or explicitly provided user feedback.
11. A user experience customizer comprising:
an input unit to gather context information;
a predictive data unit to form correlations between the context data and a likely desirable structure and content provided in a corresponding user experience, the predictive data unit further to collect user activity feedback from a community of users and to adjust the user experience customizer based on the user activity feedback; and
a decision unit to automatically produce user experience customization selections based on a correlation of the user activity feedback with the context information.
12. The user experience customizer as claimed in claim 11 wherein the context information is selected from the group: site identifier, buyer segmentation information, domain identifier, and keywords/queries.
13. The user experience customizer as claimed in claim 11 wherein the context information is selected from dimensions in the group: site identifier, buyer segmentation information, domain identifier, and keywords/queries, and the user experience customizer being further configured to progressively fall back to other sufficient and accurate dimensions in the group if a particular dimension does not provide sufficient or accurate information.
14. The user experience customizer as claimed in claim 11 wherein the user experience customization selections are selected from the group: page type, modules, and configurations.
15. The user experience customizer as claimed in claim 14 wherein the page type includes an all matching items page type.
16. The user experience customizer as claimed in claim 14 wherein the configurations include a sort order selection.
17. The user experience customizer as claimed in claim 11 being further configured to provide a perturbation engine to perturb the automatically produced user experience customization selections.
18. The user experience customizer as claimed in claim 17 wherein the perturbation engine exposes a user to user experience customization selections to which the user would not have otherwise been exposed.
19. The user experience customizer as claimed in claim 17 wherein the perturbation engine exposes a user to suboptimal user experience customization selections.
20. The user experience customizer as claimed in claim 11 wherein the predictive data unit being further configured to collect user activity feedback including clicking on items, bidding or buying activity, or explicitly provided user feedback.
21. An article of manufacture comprising a machine-readable storage medium having machine executable instructions embedded thereon, which when executed by a machine, cause the machine to:
gather context information;
provide a user experience customizer to automatically produce user experience customization selections based on the context information;
collect user activity feedback from a community of users; and
use the user activity feedback to adjust the user experience customizer to automatically produce user experience customization selections likely favored by a user based on a correlation of the user activity feedback with the context information.
22. The article of manufacture as claimed in claim 21 wherein the context information is selected from the group: site identifier, buyer segmentation information, domain identifier, and keywords/queries.
23. The article of manufacture as claimed in claim 21 wherein the context information is selected from dimensions in the group: site identifier, buyer segmentation information, domain identifier, and keywords/queries, and the article of manufacture being further configured to progressively fall back to other sufficient and accurate dimensions in the group if a particular dimension does not provide sufficient or accurate information.
24. The article of manufacture as claimed in claim 21 wherein the user experience customization selections are selected from the group: page type, modules, and configurations.
25. The article of manufacture as claimed in claim 24 wherein the page type includes an all matching items page type.
26. The article of manufacture as claimed in claim 24 wherein the configurations include a sort order selection.
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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100083217A1 (en) * 2008-09-30 2010-04-01 Dalal Vipul C System and method for orchestration of customization for a user expereince
US20130031459A1 (en) * 2011-07-27 2013-01-31 Behrooz Khorashadi Web browsing enhanced by cloud computing
US20140317006A1 (en) * 2013-04-23 2014-10-23 Jacob Andrew Brill Market specific reporting mechanisms for social content objects
WO2015108964A1 (en) * 2014-01-14 2015-07-23 Yorn, Llc Context-based feedback system and method
US10467032B2 (en) 2017-03-02 2019-11-05 International Business Machines Corporation Dynamic cloud image updates based on subjective customization and user input
WO2022005779A1 (en) * 2020-06-30 2022-01-06 Userzoom Technologies, Inc. Systems and methods for moderated user experience testing

Families Citing this family (31)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8359237B2 (en) 2008-05-23 2013-01-22 Ebay Inc. System and method for context and community based customization for a user experience
US10600073B2 (en) 2010-03-24 2020-03-24 Innovid Inc. System and method for tracking the performance of advertisements and predicting future behavior of the advertisement
US9946775B2 (en) * 2010-03-24 2018-04-17 Taykey Ltd. System and methods thereof for detection of user demographic information
US8965835B2 (en) 2010-03-24 2015-02-24 Taykey Ltd. Method for analyzing sentiment trends based on term taxonomies of user generated content
US8930377B2 (en) * 2010-03-24 2015-01-06 Taykey Ltd. System and methods thereof for mining web based user generated content for creation of term taxonomies
US9183292B2 (en) 2010-03-24 2015-11-10 Taykey Ltd. System and methods thereof for real-time detection of an hidden connection between phrases
US9613139B2 (en) 2010-03-24 2017-04-04 Taykey Ltd. System and methods thereof for real-time monitoring of a sentiment trend with respect of a desired phrase
US8782046B2 (en) 2010-03-24 2014-07-15 Taykey Ltd. System and methods for predicting future trends of term taxonomies usage
US8706812B2 (en) 2010-04-07 2014-04-22 On24, Inc. Communication console with component aggregation
US11438410B2 (en) 2010-04-07 2022-09-06 On24, Inc. Communication console with component aggregation
US9195758B2 (en) * 2011-10-21 2015-11-24 Ebay, Inc. System and method for multi-dimensional personalization of search results
US9613014B2 (en) * 2012-03-09 2017-04-04 AgileQR, Inc. Systems and methods for personalization and engagement by passive connection
US9760895B2 (en) 2012-06-04 2017-09-12 American Express Travel Related Services Company, Inc. Systems and methods for delivering tailored content based upon a consumer profile
US9355378B2 (en) 2013-03-14 2016-05-31 American Express Travel Related Services Company, Inc. Systems and methods for identifying and delivering tailored content based upon a service dialog
US9244522B2 (en) * 2013-08-30 2016-01-26 Linkedin Corporation Guided browsing experience
US11429781B1 (en) 2013-10-22 2022-08-30 On24, Inc. System and method of annotating presentation timeline with questions, comments and notes using simple user inputs in mobile devices
US11301803B1 (en) * 2014-03-17 2022-04-12 Ecmd, Inc. Inventory and sales process control and display
US10785325B1 (en) 2014-09-03 2020-09-22 On24, Inc. Audience binning system and method for webcasting and on-line presentations
US10102592B2 (en) * 2014-12-31 2018-10-16 Ebay Inc. Native selling platform
US20160217481A1 (en) * 2015-01-27 2016-07-28 Jacqueline Stetson PASTORE Communication system and server for conducting user experience study
US11282122B1 (en) * 2015-01-30 2022-03-22 MY Mavens LLC Evaluation and comparison of vendors according to structured capability models
US10146829B2 (en) * 2015-09-28 2018-12-04 Google Llc Query composition system
US10101870B2 (en) 2015-10-16 2018-10-16 Microsoft Technology Licensing, Llc Customizing program features on a per-user basis
US10607278B2 (en) * 2016-09-22 2020-03-31 Appsoft Oy System and method for populating a database with a set of information related to an item owned by a first owner that is transferred to a second owner
US10922743B1 (en) 2017-01-04 2021-02-16 Amazon Technologies, Inc. Adaptive performance of actions associated with custom user interface controls
US10664146B2 (en) 2017-01-04 2020-05-26 Amazon Technologies, Inc. Creation of custom user interface controls that are associated with physical devices
US10521854B1 (en) 2017-01-04 2019-12-31 Amazon Technologies, Inc. Selection and display of custom user interface controls
US10296195B2 (en) 2017-01-04 2019-05-21 Amazon Technologies, Inc. Automatic creation of custom user interface controls
US11281723B2 (en) * 2017-10-05 2022-03-22 On24, Inc. Widget recommendation for an online event using co-occurrence matrix
US11188822B2 (en) 2017-10-05 2021-11-30 On24, Inc. Attendee engagement determining system and method
US11195202B2 (en) * 2018-10-17 2021-12-07 Microsoft Technology Licensing, Llc Dynamic monitoring and control of web page experiences based upon user activity of associated applications

Citations (38)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020010753A1 (en) * 1999-12-20 2002-01-24 Matsuoka Robert M. Method and apparatus for delivering dynamic information in a computer network
US20020099605A1 (en) * 2000-10-06 2002-07-25 Searchcactus, Llc Search engine with demographic-based advertising
US20020138562A1 (en) * 1995-12-13 2002-09-26 Immersion Corporation Defining force sensations associated with graphical images
US20030009497A1 (en) * 2001-07-05 2003-01-09 Allen Yu Community based personalization system and method
US20040019688A1 (en) * 2002-07-29 2004-01-29 Opinionlab Providing substantially real-time access to collected information concerning user interaction with a web page of a website
US20040169678A1 (en) * 2002-11-27 2004-09-02 Oliver Huw Edward Obtaining user feedback on displayed items
US20040199584A1 (en) * 2003-03-05 2004-10-07 Evan Kirshenbaum Method and system for customized configuration of an appearance of a website for a user
US7072888B1 (en) * 1999-06-16 2006-07-04 Triogo, Inc. Process for improving search engine efficiency using feedback
US20060155689A1 (en) * 2004-07-23 2006-07-13 Oracle International Corporation Web-based user interface for searching metadata-driven relational databases
US20060161553A1 (en) * 2005-01-19 2006-07-20 Tiny Engine, Inc. Systems and methods for providing user interaction based profiles
US7171629B2 (en) * 2000-10-20 2007-01-30 Adaptive Avenue Associates, Inc. Customizable web site access system and method therefore
US20070078828A1 (en) * 2005-10-05 2007-04-05 Yahoo! Inc. Customizable ordering of search results and predictive query generation
US20070100821A1 (en) * 2005-10-31 2007-05-03 Freeman Jackie A Presentation of differences between multiple searches
US20070100797A1 (en) * 2005-10-31 2007-05-03 Christopher Thun Indication of exclusive items in a result set
US20070244914A1 (en) * 2006-04-17 2007-10-18 Kreiner Barrett M Electronic information feedback
US20070266025A1 (en) * 2006-05-12 2007-11-15 Microsoft Corporation Implicit tokenized result ranking
US20070276803A1 (en) * 2006-05-24 2007-11-29 Microsoft Corporation Inline search results from user-created search verticals
US20080033790A1 (en) * 2006-08-02 2008-02-07 Nickerson Rand B System and Method for Measuring and Reporting User Reactions to Advertisements on a Web Page
US20080059455A1 (en) * 2006-08-31 2008-03-06 Canoy Michael-David N Method and apparatus of obtaining or providing search results using user-based biases
US20080092060A1 (en) * 2006-10-17 2008-04-17 Jordan Berg System and method for capturing website user feedback
US20080147710A1 (en) * 2006-12-19 2008-06-19 Microsoft Corporation Generating web pages utilizing user-defined search parameters
US20080172326A1 (en) * 2007-01-17 2008-07-17 Michelle Winters Method and system for generating and sharing customized portable consumer devices
US20080235608A1 (en) * 2007-03-20 2008-09-25 Microsoft Corporation Customizable layout of search results
US20080301093A1 (en) * 2007-06-01 2008-12-04 Google Inc. Determining Search Query Statistical Data for an Advertising Campaign Based on User-Selected Criteria
US20090019053A1 (en) * 2007-07-13 2009-01-15 Yahoo! Inc. Method for searching for and marketing fashion garments online
US20090055384A1 (en) * 2007-08-23 2009-02-26 Yahoo! Inc. Shared influence search
US20090077056A1 (en) * 2007-09-17 2009-03-19 Yahoo! Inc. Customization of search results
US20090113346A1 (en) * 2007-10-30 2009-04-30 Motorola, Inc. Method and apparatus for context-aware delivery of informational content on ambient displays
US20090164408A1 (en) * 2007-12-21 2009-06-25 Ilya Grigorik Method, System and Computer Program for Managing Delivery of Online Content
US20090164929A1 (en) * 2007-12-20 2009-06-25 Microsoft Corporation Customizing Search Results
US20090216760A1 (en) * 2007-08-29 2009-08-27 Bennett James D Search engine with webpage rating feedback based internet search operation
US20090282002A1 (en) * 2008-03-10 2009-11-12 Travis Reeder Methods and systems for integrating data from social networks
US7801885B1 (en) * 2007-01-25 2010-09-21 Neal Akash Verma Search engine system and method with user feedback on search results
US7979457B1 (en) * 2005-03-02 2011-07-12 Kayak Software Corporation Efficient search of supplier servers based on stored search results
US20120023081A1 (en) * 2010-07-26 2012-01-26 Microsoft Corporation Customizing search home pages using interest indicators
US8166026B1 (en) * 2006-12-26 2012-04-24 uAffect.org LLC User-centric, user-weighted method and apparatus for improving relevance and analysis of information sharing and searching
US20120226995A1 (en) * 2011-03-02 2012-09-06 Microsoft Corporation Content Customization with Security for Client Preferences
US20140108968A1 (en) * 2012-10-11 2014-04-17 Yahoo! Inc. Visual Presentation of Customized Content

Family Cites Families (18)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5848396A (en) * 1996-04-26 1998-12-08 Freedom Of Information, Inc. Method and apparatus for determining behavioral profile of a computer user
US6842877B2 (en) * 1998-12-18 2005-01-11 Tangis Corporation Contextual responses based on automated learning techniques
US6981040B1 (en) * 1999-12-28 2005-12-27 Utopy, Inc. Automatic, personalized online information and product services
US6631184B1 (en) * 2000-07-24 2003-10-07 Comverse Ltd. System for community generated feedback and/or rating
US6564170B2 (en) * 2000-12-29 2003-05-13 Hewlett-Packard Development Company, L.P. Customizable user interfaces
US7263663B2 (en) * 2001-03-02 2007-08-28 Oracle International Corporation Customization of user interface presentation in an internet application user interface
NO316480B1 (en) * 2001-11-15 2004-01-26 Forinnova As Method and system for textual examination and discovery
US7249321B2 (en) * 2002-10-03 2007-07-24 At&T Knowlege Ventures, L.P. System and method for selection of a voice user interface dialogue
US7509649B2 (en) 2005-03-03 2009-03-24 Research In Motion Limited System and method for conversion of generic services' applications into component based applications for devices
US8099178B2 (en) * 2005-08-22 2012-01-17 Trane International Inc. Building automation system facilitating user customization
US8595633B2 (en) 2005-10-31 2013-11-26 Yahoo! Inc. Method and system for displaying contextual rotating advertisements
US20080021918A1 (en) * 2005-12-23 2008-01-24 Rao Viswanatha H Enterprise service management unifier system
US20070174390A1 (en) * 2006-01-20 2007-07-26 Avise Partners Customer service management
US7805441B2 (en) 2006-03-06 2010-09-28 Yahoo! Inc. Vertical search expansion, disambiguation, and optimization of search queries
US7505995B2 (en) 2006-06-30 2009-03-17 Microsoft Corporation Object-relational model based user interfaces
US7765236B2 (en) 2007-08-31 2010-07-27 Microsoft Corporation Extracting data content items using template matching
US8359237B2 (en) 2008-05-23 2013-01-22 Ebay Inc. System and method for context and community based customization for a user experience
US8904345B2 (en) 2008-09-30 2014-12-02 Ebay Inc. System and method for orchestration of customization for a user experience

Patent Citations (38)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020138562A1 (en) * 1995-12-13 2002-09-26 Immersion Corporation Defining force sensations associated with graphical images
US7072888B1 (en) * 1999-06-16 2006-07-04 Triogo, Inc. Process for improving search engine efficiency using feedback
US20020010753A1 (en) * 1999-12-20 2002-01-24 Matsuoka Robert M. Method and apparatus for delivering dynamic information in a computer network
US20020099605A1 (en) * 2000-10-06 2002-07-25 Searchcactus, Llc Search engine with demographic-based advertising
US7171629B2 (en) * 2000-10-20 2007-01-30 Adaptive Avenue Associates, Inc. Customizable web site access system and method therefore
US20030009497A1 (en) * 2001-07-05 2003-01-09 Allen Yu Community based personalization system and method
US20040019688A1 (en) * 2002-07-29 2004-01-29 Opinionlab Providing substantially real-time access to collected information concerning user interaction with a web page of a website
US20040169678A1 (en) * 2002-11-27 2004-09-02 Oliver Huw Edward Obtaining user feedback on displayed items
US20040199584A1 (en) * 2003-03-05 2004-10-07 Evan Kirshenbaum Method and system for customized configuration of an appearance of a website for a user
US20060155689A1 (en) * 2004-07-23 2006-07-13 Oracle International Corporation Web-based user interface for searching metadata-driven relational databases
US20060161553A1 (en) * 2005-01-19 2006-07-20 Tiny Engine, Inc. Systems and methods for providing user interaction based profiles
US7979457B1 (en) * 2005-03-02 2011-07-12 Kayak Software Corporation Efficient search of supplier servers based on stored search results
US20070078828A1 (en) * 2005-10-05 2007-04-05 Yahoo! Inc. Customizable ordering of search results and predictive query generation
US20070100821A1 (en) * 2005-10-31 2007-05-03 Freeman Jackie A Presentation of differences between multiple searches
US20070100797A1 (en) * 2005-10-31 2007-05-03 Christopher Thun Indication of exclusive items in a result set
US20070244914A1 (en) * 2006-04-17 2007-10-18 Kreiner Barrett M Electronic information feedback
US20070266025A1 (en) * 2006-05-12 2007-11-15 Microsoft Corporation Implicit tokenized result ranking
US20070276803A1 (en) * 2006-05-24 2007-11-29 Microsoft Corporation Inline search results from user-created search verticals
US20080033790A1 (en) * 2006-08-02 2008-02-07 Nickerson Rand B System and Method for Measuring and Reporting User Reactions to Advertisements on a Web Page
US20080059455A1 (en) * 2006-08-31 2008-03-06 Canoy Michael-David N Method and apparatus of obtaining or providing search results using user-based biases
US20080092060A1 (en) * 2006-10-17 2008-04-17 Jordan Berg System and method for capturing website user feedback
US20080147710A1 (en) * 2006-12-19 2008-06-19 Microsoft Corporation Generating web pages utilizing user-defined search parameters
US8166026B1 (en) * 2006-12-26 2012-04-24 uAffect.org LLC User-centric, user-weighted method and apparatus for improving relevance and analysis of information sharing and searching
US20080172326A1 (en) * 2007-01-17 2008-07-17 Michelle Winters Method and system for generating and sharing customized portable consumer devices
US7801885B1 (en) * 2007-01-25 2010-09-21 Neal Akash Verma Search engine system and method with user feedback on search results
US20080235608A1 (en) * 2007-03-20 2008-09-25 Microsoft Corporation Customizable layout of search results
US20080301093A1 (en) * 2007-06-01 2008-12-04 Google Inc. Determining Search Query Statistical Data for an Advertising Campaign Based on User-Selected Criteria
US20090019053A1 (en) * 2007-07-13 2009-01-15 Yahoo! Inc. Method for searching for and marketing fashion garments online
US20090055384A1 (en) * 2007-08-23 2009-02-26 Yahoo! Inc. Shared influence search
US20090216760A1 (en) * 2007-08-29 2009-08-27 Bennett James D Search engine with webpage rating feedback based internet search operation
US20090077056A1 (en) * 2007-09-17 2009-03-19 Yahoo! Inc. Customization of search results
US20090113346A1 (en) * 2007-10-30 2009-04-30 Motorola, Inc. Method and apparatus for context-aware delivery of informational content on ambient displays
US20090164929A1 (en) * 2007-12-20 2009-06-25 Microsoft Corporation Customizing Search Results
US20090164408A1 (en) * 2007-12-21 2009-06-25 Ilya Grigorik Method, System and Computer Program for Managing Delivery of Online Content
US20090282002A1 (en) * 2008-03-10 2009-11-12 Travis Reeder Methods and systems for integrating data from social networks
US20120023081A1 (en) * 2010-07-26 2012-01-26 Microsoft Corporation Customizing search home pages using interest indicators
US20120226995A1 (en) * 2011-03-02 2012-09-06 Microsoft Corporation Content Customization with Security for Client Preferences
US20140108968A1 (en) * 2012-10-11 2014-04-17 Yahoo! Inc. Visual Presentation of Customized Content

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100083217A1 (en) * 2008-09-30 2010-04-01 Dalal Vipul C System and method for orchestration of customization for a user expereince
US8904345B2 (en) 2008-09-30 2014-12-02 Ebay Inc. System and method for orchestration of customization for a user experience
US9753902B2 (en) 2008-09-30 2017-09-05 Ebay Inc. System and method for orchestration of customization for a user experience
US20130031459A1 (en) * 2011-07-27 2013-01-31 Behrooz Khorashadi Web browsing enhanced by cloud computing
US9146909B2 (en) * 2011-07-27 2015-09-29 Qualcomm Incorporated Web browsing enhanced by cloud computing
US20140317006A1 (en) * 2013-04-23 2014-10-23 Jacob Andrew Brill Market specific reporting mechanisms for social content objects
WO2015108964A1 (en) * 2014-01-14 2015-07-23 Yorn, Llc Context-based feedback system and method
US11574346B2 (en) 2014-01-14 2023-02-07 Clx Health Llc Context-based feedback system and method
US10467032B2 (en) 2017-03-02 2019-11-05 International Business Machines Corporation Dynamic cloud image updates based on subjective customization and user input
WO2022005779A1 (en) * 2020-06-30 2022-01-06 Userzoom Technologies, Inc. Systems and methods for moderated user experience testing

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