WO2017201907A1

WO2017201907A1 - Search term classification method and device

Info

Publication number: WO2017201907A1
Application number: PCT/CN2016/097351
Authority: WO
Inventors: 马守玉
Original assignee: 百度在线网络技术（北京）有限公司
Priority date: 2016-05-24
Filing date: 2016-08-30
Publication date: 2017-11-30
Also published as: CN107423304A

Abstract

A search term classification method and device. The search term classification method comprises the following steps: receiving a search term inputted by a user, and acquiring, according to the search term, corresponding search results (S1); acquiring click data of the search results, and extracting corresponding data features according to the click data (S2); training with the data features to generate a classifier (S3); and classifying a search term according to the classifier (S4). The method and device of the present invention enrich search results, and improve variety and expandability of the search results.

Description

Search term classification method and device

Cross-reference to related applications

This application claims the priority of the Chinese patent application No. "201610350036.1" filed on May 24, 2016 by Baidu Online Network Technology (Beijing) Co., Ltd., entitled "Search Method and Apparatus for Searching Terms".

Technical field

The present invention relates to the field of computer technologies, and in particular, to a method and apparatus for classifying search terms.

Background technique

With the rapid development of the Internet, mobile terminals such as smart phones are becoming more and more popular. In the process of using a smart phone, users can install applications with various functions according to requirements.

Currently, users can search for the desired APP (Application) by entering the search term through the app store. However, the results obtained by this method are only related to the search terms, which are relatively simple and not rich enough.

Summary of the invention

The present invention aims to solve the above technical problems at least to some extent.

To this end, the first object of the present invention is to propose a method for classifying search terms, which can enrich search results and improve the diversity and extensibility of search results.

A second object of the present invention is to provide a search term classification device.

In order to achieve the above objective, a search term classification method according to the first aspect of the present invention includes: receiving a search term input by a user, and obtaining a corresponding search result according to the search term; and obtaining a click of the search result. Data, and extracting corresponding data features according to the click data; training the data features to generate a classifier; and classifying the search terms according to the classifier.

The method for classifying a search term according to an embodiment of the present invention receives a search term input by a user, and obtains a corresponding search result according to the search term, acquires click data of the search result, and extracts corresponding data according to the click data. The feature, the data feature is trained to generate a classifier, and the search term is classified according to the classifier, which can enrich the search result and improve the diversity and extensibility of the search result.

The second aspect of the present invention provides a search term classification device, including: an obtaining module, configured to receive a search term input by a user, and obtain a corresponding search result according to the search term; and an extracting module, configured to acquire the Search knot Clicking data, and extracting corresponding data features according to the click data; a training module for training the data features to generate a classifier; and a classification module for performing the search term according to the classifier classification.

The search term classification device of the embodiment of the present invention receives the search term input by the user, acquires the corresponding search result according to the search term, acquires the click data of the search result, and extracts corresponding data according to the click data. The feature, the data feature is trained to generate a classifier, and the search term is classified according to the classifier, which can enrich the search result and improve the diversity and extensibility of the search result.

An embodiment of the third aspect of the present invention provides an electronic device comprising: one or more processors; a memory; one or more programs, the one or more programs being stored in the memory when Or when the plurality of processors are executed, the search term classification method of the first aspect of the present invention is executed.

A fourth aspect of the present invention provides a non-volatile computer storage medium storing one or more programs, when the one or more programs are executed by one device, causing the device A search term classification method in accordance with an embodiment of the first aspect of the present invention is performed.

The additional aspects and advantages of the invention will be set forth in part in the description which follows.

DRAWINGS

The above and/or additional aspects and advantages of the present invention will become apparent and readily understood from

1 is a flow chart of a method for classifying search terms according to an embodiment of the present invention;

2 is a block diagram showing the structure of a search term classification device according to an embodiment of the present invention.

detailed description

The embodiments of the present invention are described in detail below, and the examples of the embodiments are illustrated in the drawings, wherein the same or similar reference numerals are used to refer to the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are intended to be illustrative of the invention and are not to be construed as limiting.

In the description of the present invention, it is to be understood that the term "plurality" means two or more; the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying that they are relatively important. Sex.

A search term classification method and apparatus according to an embodiment of the present invention will be described below with reference to the accompanying drawings.

A search term classification method includes the following steps: receiving a search term input by a user, and obtaining a corresponding search result according to the search term; acquiring click data of the search result, and extracting corresponding data features according to the click data Training the data features to generate a classifier; and classifying the search terms according to the classifier.

1 is a flow chart of a method for classifying search terms in accordance with one embodiment of the present invention.

As shown in FIG. 1, a search term classification method according to an embodiment of the present invention includes the following steps.

S1. Receive a search term input by the user, and obtain a corresponding search result according to the search term.

For example, the user can enter the application store and input the search term “flowers” in the search bar of the application store, and then search the application store according to the search term “flowers” to obtain the application APP related to “flowers”. Such as flower nets, flowers and so on.

S2: Obtain click data of the search result, and extract corresponding data features according to the click data.

After obtaining the search result, the user may receive a click operation on the search result, and record the click data corresponding to the click operation. The corresponding data features can then be extracted based on the click data. The data characteristics may include a name, a category, a keyword, a version, a file size, a download count, a developer name, and the like of the search result. For example, if the clicked APP is a flower and a Lianliankan, you can extract the name of the flower Lianliankan APP, “Flower Lianliankan”, category “Game”, version “6.0”, file size “30M”, download times 500 times, etc.

S3, training data features to generate a classifier.

After extracting the data features, the machine learning model can be used to train the data features to generate a classifier. Among them, the machine learning model may include a naive Bayesian model, a support vector machine model, a neural network, and the like. For example, the data feature of the category of the APP can be used to train using the naive Bayesian model to generate a classifier. Alternatively, the classifier and the keyword of the APP can be used to train using the support vector machine model to generate a classifier.

S4, classifying the search words according to the classifier.

After training the classifier, the classifier can be used to classify the search terms. For example, the search term "flowers" can be classified into games by the classifier.

When the user searches again with the search term "flowers", more APPs belonging to the game category can be recommended to the user, thereby enriching the search results.

Of course, the classification of search terms is not limited to one category and can belong to multiple categories. Priority can be given to users to categories that have a high number of clicks.

The search term classification method according to the embodiment of the present invention receives the search term input by the user, obtains the corresponding search result according to the search term, acquires the click data of the search result, and extracts the corresponding data feature according to the click data, and trains the data feature. By generating a classifier and classifying the search words according to the classifier, the search results can be enriched, and the diversity and extensibility of the search results can be improved.

In order to implement the above embodiment, the present invention also proposes a search term classification device.

A search term classification device, comprising: an obtaining module, configured to receive a search term input by a user, and obtain a corresponding search result according to the search term; and an extracting module, configured to acquire click data of the search result, and according to the a data feature corresponding to the click data extraction; a training module for training the data feature to generate a classifier; a class module for classifying the search terms according to the classifier.

As shown in FIG. 2, the search term classification device according to an embodiment of the present invention includes: an acquisition module 110, an extraction module 120, a training module 130, and a classification module 140.

The obtaining module 110 is configured to receive a search term input by the user, and obtain a corresponding search result according to the search term. For example, the user can enter the application store and input the search term “flowers” in the search bar of the application store, and then search the application store according to the search term “flowers” to obtain the application APP related to “flowers”. Such as flower nets, flowers and so on.

The extraction module 120 is configured to obtain click data of the retrieval result, and extract corresponding data features according to the click data. After obtaining the search result, the user may receive a click operation on the search result, and record the click data corresponding to the click operation. The corresponding data features can then be extracted based on the click data. The data characteristics may include a name, a category, a keyword, a version, a file size, a download count, a developer name, and the like of the search result. For example, if the clicked APP is a flower and a Lianliankan, you can extract the name of the flower Lianliankan APP, “Flower Lianliankan”, category “Game”, version “6.0”, file size “30M”, download times 500 times, etc.

Training module 130 is used to train data features to generate a classifier. After extracting the data features, the machine learning model can be used to train the data features to generate a classifier. Among them, the machine learning model may include a naive Bayesian model, a support vector machine model, a neural network, and the like. For example, the data feature of the category of the APP can be used to train using the naive Bayesian model to generate a classifier. Alternatively, the classifier and the keyword of the APP can be used to train using the support vector machine model to generate a classifier.

The classification module 140 is configured to classify the search terms according to the classifier. After training the classifier, the classifier can be used to classify the search terms. For example, the search term "flowers" can be classified into games by the classifier.

The search term classification device of the embodiment of the present invention receives the search term input by the user, obtains the corresponding search result according to the search term, acquires the click data of the search result, and extracts the corresponding data feature according to the click data, and trains the data feature. By generating a classifier and classifying the search words according to the classifier, the search results can be enriched, and the diversity and extensibility of the search results can be improved.

In the description of the present specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" and the like means a specific feature described in connection with the embodiment or example. , structure, material or Features are included in at least one embodiment or example of the present invention. In the present specification, the schematic representation of the above terms is not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. In addition, various embodiments or examples described in the specification, as well as features of various embodiments or examples, may be combined and combined.

Moreover, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" or "second" may include at least one of the features, either explicitly or implicitly. In the description of the present invention, the meaning of "a plurality" is two or more unless specifically and specifically defined otherwise.

Any process or method description in the flowcharts or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a particular logical function or process. And the scope of the preferred embodiments of the invention includes additional implementations, in which the functions may be performed in a substantially simultaneous manner or in an opposite order depending on the functions involved, in the order shown or discussed. It will be understood by those skilled in the art to which the embodiments of the present invention pertain.

The logic and/or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be embodied in any computer readable medium, Used in conjunction with, or in conjunction with, an instruction execution system, apparatus, or device (eg, a computer-based system, a system including a processor, or other system that can fetch instructions and execute instructions from an instruction execution system, apparatus, or device) Or use with equipment. For the purposes of this specification, a "computer-readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport a program for use in an instruction execution system, apparatus, or device, or in conjunction with the instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer readable media include the following: electrical connections (electronic devices) having one or more wires, portable computer disk cartridges (magnetic devices), random access memory (RAM), Read only memory (ROM), erasable editable read only memory (EPROM or flash memory), fiber optic devices, and portable compact disk read only memory (CDROM). In addition, the computer readable medium may even be a paper or other suitable medium on which the program can be printed, as it may be optically scanned, for example by paper or other medium, followed by editing, interpretation or, if appropriate, other suitable The method is processed to obtain the program electronically and then stored in computer memory.

It should be understood that portions of the invention may be implemented in hardware, software, firmware or a combination thereof. In the above-described embodiments, multiple steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented by any one or combination of the following techniques well known in the art: having logic gates for implementing logic functions on data signals. Discrete logic circuit, ASIC with suitable combinational logic gate, Programmable Gate Array (PGA), now Field programmable gate array (FPGA), etc.

One of ordinary skill in the art can understand that all or part of the steps carried by the method of implementing the above embodiments can be completed by a program to instruct related hardware, and the program can be stored in a computer readable storage medium. When executed, one or a combination of the steps of the method embodiments is included.

In addition, each functional unit in each embodiment of the present invention may be integrated into one processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules. The integrated modules, if implemented in the form of software functional modules and sold or used as stand-alone products, may also be stored in a computer readable storage medium.

The above mentioned storage medium may be a read only memory, a magnetic disk or an optical disk or the like. Although the embodiments of the present invention have been shown and described, it is understood that the above-described embodiments are illustrative and are not to be construed as limiting the scope of the invention. The embodiments are subject to variations, modifications, substitutions and variations.

Claims

A method for classifying search terms, including:

Receiving a search term input by the user, and obtaining a corresponding search result according to the search term;

Obtaining click data of the search result, and extracting corresponding data features according to the click data;

Training the data features to generate a classifier;

The search terms are classified according to the classifier.
The method of claim 1, wherein the data feature comprises one or more of a name, a category, a keyword, a version, a file size, a number of downloads, and a developer name of the search result.
The method of claim 1 wherein training the data feature to generate a classifier comprises:

The data features are trained using a machine learning model to generate a classifier.
The method of claim 3, wherein the machine learning model comprises one of a naive Bayesian model, a support vector machine model, and a neural network.
A search term classification device comprising:

An obtaining module, configured to receive a search term input by a user, and obtain a corresponding search result according to the search term;

An extraction module, configured to acquire click data of the search result, and extract corresponding data features according to the click data;

a training module for training the data features to generate a classifier;

a classification module, configured to classify the search terms according to the classifier.
The apparatus of claim 5, wherein the data feature comprises one or more of a name, a category, a keyword, a version, a file size, a download count, and a developer name of the search result.
The apparatus of claim 5 wherein said training module is configured to:

The data features are trained using a machine learning model to generate a classifier.
The apparatus of claim 7, wherein the machine learning model comprises one of a naive Bayesian model, a support vector machine model, and a neural network.
An electronic device, comprising:

One or more processors;

Memory

One or more programs, the one or more programs being stored in the memory, and when executed by the one or more processors, performing the search term classification method according to any one of claims 1-4 .
A non-volatile computer storage medium, characterized in that the computer storage medium stores one or more programs, when the one or more programs are executed by a device, causing the device to perform The search term classification method according to any one of claims 1 to 4.