CN111292114B - Method and device for generating labels - Google Patents

Method and device for generating labels Download PDF

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Publication number
CN111292114B
CN111292114B CN201811504653.8A CN201811504653A CN111292114B CN 111292114 B CN111292114 B CN 111292114B CN 201811504653 A CN201811504653 A CN 201811504653A CN 111292114 B CN111292114 B CN 111292114B
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application
related information
category
name list
crowd category
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CN111292114A (en
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文灿
顾静航
李春林
周俊
康建峰
姚远
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Beijing Baidu Netcom Science and Technology Co Ltd
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Beijing Baidu Netcom Science and Technology Co Ltd
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    • 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
    • G06Q30/0202Market predictions or forecasting for commercial activities
    • 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

Abstract

The embodiment of the application discloses a method and a device for generating labels. One embodiment of the method includes obtaining an application name list of applications operated by a user during a historical period of time; acquiring related information of an application in an application name list; determining the group category of the user based on the application name list and the related information; and generating a corresponding crowd category label based on the crowd category to which the user belongs. According to the embodiment, the crowd category of the user is determined based on the application operated by the user, so that the accuracy of the determined crowd category is improved, and the accuracy and the refinement degree of the characterization of the user attribute are improved.

Description

Method and device for generating labels
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to a method and a device for generating labels.
Background
The user representation may be user information including descriptive labels of at least one user attribute established from pre-accumulated multi-source user data. Descriptive labels may be used to describe user attributes including, but not limited to, gender, age, marital status, occupation, asset status, education level, and the like. Understanding the user attributes of the various dimensions in the user representation may mine points of interest for the user, including but not limited to travel, games, sports games, and the like. However, due to the difficulty in acquiring the user data for creating the user portrait, and the authenticity and accuracy of the user data cannot be evaluated, the user attributes are not accurately and finely described.
Disclosure of Invention
The embodiment of the application provides a method and a device for generating labels.
In a first aspect, an embodiment of the present application provides a method for generating a tag, including: acquiring an application name list of an application operated by a user in a historical time period; acquiring related information of an application in an application name list; determining the group category of the user based on the application name list and the related information; and generating a corresponding crowd category label based on the crowd category to which the user belongs.
In some embodiments, determining the group of people to which the user belongs based on the list of application names and related information includes: extracting keywords in the application name list and related information by using a natural language processing technology; and matching the keywords in the application name list and the related information in a preset first person group category set to obtain a successfully matched first person group category which is used as the group category to which the user belongs.
In some embodiments, determining the group of people to which the user belongs based on the list of application names and related information includes: identifying the prefix and/or the suffix of the application name in the application name list by using the Chinese language model to obtain an identification result; and matching the identification result in a preset second crowd category set to obtain a second crowd category successfully matched with the identification result, wherein the second crowd category is used as the crowd category to which the user belongs.
In some embodiments, determining the group of people to which the user belongs based on the list of application names and related information includes: carrying out statement structure analysis on the statements in the related information to determine the statements conforming to the preset statement structure; and matching the sentences conforming to the preset sentence structure in a second crowd category set to obtain a second crowd category successfully matched with the sentences conforming to the preset sentence structure, wherein the second crowd category is used as the crowd category to which the user belongs.
In some embodiments, determining the group of people to which the user belongs based on the list of application names and related information includes: extracting keywords in the related information by using a natural language processing technology; and classifying the application in the name list by taking the application names in the application name list and the keywords in the related information as classification features, and determining the crowd category to which the user belongs based on the classification result.
In a second aspect, an embodiment of the present application provides an apparatus for generating a tag, including: a first acquisition unit configured to acquire an application name list of an application operated by a user in a history period; a second acquisition unit configured to acquire related information of an application in the application name list; a determining unit configured to determine a group category to which the user belongs based on the application name list and the related information; and the generation unit is configured to generate a corresponding crowd category label based on the crowd category to which the user belongs.
In some embodiments, the determining unit is further configured to: extracting keywords in the application name list and related information by using a natural language processing technology; and matching the keywords in the application name list and the related information in a preset first person group category set to obtain a successfully matched first person group category which is used as the group category to which the user belongs.
In some embodiments, the determining unit is further configured to: identifying the prefix and/or the suffix of the application name in the application name list by using the Chinese language model to obtain an identification result; and matching the identification result in a preset second crowd category set to obtain a second crowd category successfully matched with the identification result, wherein the second crowd category is used as the crowd category to which the user belongs.
In some embodiments, the determining unit is further configured to: carrying out statement structure analysis on the statements in the related information to determine the statements conforming to the preset statement structure; and matching the sentences conforming to the preset sentence structure in a second crowd category set to obtain a second crowd category successfully matched with the sentences conforming to the preset sentence structure, wherein the second crowd category is used as the crowd category to which the user belongs.
In some embodiments, the determining unit is further configured to: extracting keywords in the related information by using a natural language processing technology; and classifying the application in the name list by taking the application names in the application name list and the keywords in the related information as classification features, and determining the crowd category to which the user belongs based on the classification result.
In a third aspect, an embodiment of the present application provides an electronic device, including: one or more processors; a storage device having one or more programs stored thereon; the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method as described in any of the implementations of the first aspect.
In a fourth aspect, embodiments of the present application provide a computer readable medium having stored thereon a computer program which, when executed by a processor, implements a method as described in any of the implementations of the first aspect.
The method and the device for generating the label firstly acquire an application name list of the application operated by the user in the historical time period; then acquiring the related information of the application in the application name list; and finally, determining the crowd category to which the user belongs based on the application name list and the related information, and generating a corresponding crowd category label based on the crowd category to which the user belongs. The crowd category of the user is determined based on the application operated by the user, accuracy of the determined crowd category is improved, and the user attribute describing precision and refinement degree are improved.
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Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the following drawings, in which:
FIG. 1 is an exemplary system architecture in which the present application may be applied;
FIG. 2 is a flow chart of one embodiment of a method for generating a tag according to the present application;
FIG. 3 is a flow chart of yet another embodiment of a method for generating a tag according to the present application;
FIG. 4 is a flow chart of yet another embodiment of a method for generating a tag according to the present application;
FIG. 5 is a flow chart of another embodiment of a method for generating a tag according to the present application;
FIG. 6 is a schematic structural view of one embodiment of an apparatus for generating tags according to the present application;
fig. 7 is a schematic diagram of a computer system suitable for use in implementing embodiments of the present application.
Detailed Description
The present application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 illustrates an exemplary system architecture 100 to which embodiments of the methods for generating tags or the apparatus for generating tags of the present application may be applied.
As shown in fig. 1, servers 101, 102 and network 103 may be included in system architecture 100. Network 103 is the medium used to provide communication links between servers 101 and 102. The network 103 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
Server 101 may interact with server 102 over network 103 to receive or send messages, etc. The server 101 may be a background server of an application market or a mobile phone assistant, which may store application names of various applications.
The server 102 may provide various services, for example, the server 102 may perform processing such as analysis on data such as an application name list of an application that has been operated by a user in a history period acquired from the server 101, and generate a processing result (for example, a crowd category label).
The servers 101 and 102 may be hardware or software. When the servers 101 and 102 are hardware, they may be realized as a distributed server cluster composed of a plurality of servers, or may be realized as a single server. When the servers 101, 102 are software, they may be implemented as multiple software or software modules (e.g., to provide distributed services), or as a single software or software module. The present invention is not particularly limited herein.
It should be noted that, the method for generating a tag provided in the embodiments of the present application is generally performed by the server 102, and accordingly, the apparatus for generating a tag is generally disposed in the server 102.
It should be understood that the number of servers and networks in fig. 1 is merely illustrative. There may be any number of servers and networks, as desired for implementation.
With continued reference to fig. 2, a flow 200 of one embodiment of a method for generating tags according to the present application is shown. The method for generating a tag comprises the following steps:
step 201, an application name list of an application operated by a user in a history period is obtained.
In this embodiment, the execution subject of the method for generating a tag (e.g., the server 102 shown in fig. 1) may acquire the application name list of the application operated by the user in the history period (e.g., in the first three months) from the application market or the background server of the mobile phone assistant (e.g., the server 101 shown in fig. 1) through a wired connection or a wireless connection. The applications that the user has operated may include, but are not limited to, applications that the user has downloaded, applications that the user has installed, applications that the user has used, applications that the user has updated, applications that the user has uninstalled, and so forth. The application name may include, but is not limited to, the name of the application, the name of the installation package of the application, and so forth. In general, when a user operates an application, information of the application operated by the user may be fed back to a background server of the application marketplace or the mobile phone assistant so that the background server of the application marketplace or the mobile phone assistant records an application name of the application in an application name list of the application operated by the user. Therefore, the execution subject can acquire the application name list of the application operated by the user in the history period from the application market or the background server of the mobile phone assistant.
Step 202, acquiring related information of an application in an application name list.
In this embodiment, the execution body may acquire the relevant information of the application in the application name list from a background server of the application market or a mobile phone assistant, a background server of the encyclopedia application, or a background server of the search application through a wired connection manner or a wireless connection manner. The relevant information of the application may include, but is not limited to, introduction information of the application, evaluation information of the application by a user, and the like. In general, the background server of the application market or the mobile phone assistant may further store introduction information of an application and evaluation information of a user on the application, so that the executing body may acquire the application name list from the background server of the application market or the mobile phone assistant, and simultaneously, may also acquire introduction information of an application of the application in the application name list and evaluation information of the user on the application. The execution body may search on the background server of the encyclopedia application or the background server of the search application based on the application name in the application name list, so as to obtain the related information of the application in the application name list.
Step 203, determining the crowd category to which the user belongs based on the application name list and the related information.
In this embodiment, the execution body may analyze the application name list and the related information to determine the crowd category to which the user belongs. For example, if it is determined that the application is a cloud service application of a brand of automobile according to the application name and related information of the application, the user belongs to an owner of the brand of automobile. For another example, if it is determined that the application is a driver-side application of a driver, according to the application name and related information of the application, the user belongs to the driver of the driver.
Step 204, generating a corresponding crowd category label based on the crowd category to which the user belongs.
In this embodiment, the executing body may generate the corresponding crowd category label based on the crowd category to which the user belongs.
In some optional implementations of this embodiment, the execution entity may generate the user portrait of the user based on the crowd category labels of the user, thereby improving accuracy in describing attributes of the user by the user portrait. The executing entity can then determine the interest points of the user based on the user representation, thereby improving the accuracy of the determined interest points. Finally, the execution main body can push information for the user according to the interest points of the user, so that the accuracy of information push is improved.
The method for generating the label includes the steps that firstly, an application name list of an application operated by a user in a historical time period is obtained; then acquiring the related information of the application in the application name list; and finally, determining the crowd category to which the user belongs based on the application name list and the related information, and generating a corresponding crowd category label based on the crowd category to which the user belongs. The crowd category of the user is determined based on the application operated by the user, accuracy of the determined crowd category is improved, and the user attribute describing precision and refinement degree are improved.
With further reference to fig. 3, a flow 300 of yet another embodiment of a method for generating tags according to the present application is shown. The method for generating a tag comprises the following steps:
step 301, acquiring an application name list of an application operated by a user in a historical time period.
Step 302, acquiring related information of an application in an application name list.
In this embodiment, the specific operations of steps 301 to 302 are substantially the same as those of steps 201 to 202 in the embodiment shown in fig. 2, and will not be described herein.
Step 303, extracting keywords in the application name list and related information by using natural language processing technology.
In this embodiment, an execution subject of the method for generating a tag (e.g., the server 102 shown in fig. 1) may extract keywords from the application name list and related information. In general, the execution subject may extract keywords in the application name list and related information using NLP (Natural Language Processing ) technology. Among these, NLP is a way for a computer to analyze, understand and obtain meaning from human language in a clever and useful way. By utilizing NLP, developers can organize and build knowledge to perform tasks such as automatic abstracting, translating, named entity recognition, relation extraction, emotion analysis, voice recognition, topic segmentation and the like.
Step 304, matching the keywords in the application name list and the related information in a preset first person group category set to obtain a successfully matched first person group category, wherein the successfully matched first person group category is used as the group category to which the user belongs.
In this embodiment, the execution body may preset the first group category set. For example, the executive may generate a first set of person categories based on various brands of automobiles. At this time, the first group category set includes categories of owners of various brands of automobiles. Then, the executing body may match the keywords in the application name list and the related information with each first person group category set in the first person group category sets one by one, if the keywords in the application name list and the related information are the same as the automobile brands corresponding to one first person group category, the matching is successful, and the first person group category is the successfully matched first person group category, that is, the crowd category to which the user belongs.
Step 305, generating a corresponding crowd category label based on the crowd category to which the user belongs.
In this embodiment, the specific operation of step 305 is substantially the same as that of step 204 in the embodiment shown in fig. 2, and will not be described herein.
As can be seen from fig. 3, compared with the embodiment corresponding to fig. 2, the process 300 of the method for generating a label in this embodiment highlights the step of matching the keyword in the application name list and the related information in the first group category set to obtain the group category to which the user belongs. Therefore, the crowd category of the user is obtained based on keyword matching, and the accuracy of the determined crowd category is further improved.
With further reference to fig. 4, a flow 400 of yet another embodiment of a method for generating tags according to the present application is shown. The method for generating a tag comprises the following steps:
step 401, acquiring an application name list of an application operated by a user in a historical time period.
Step 402, acquiring related information of an application in an application name list.
In this embodiment, the specific operations of steps 401 to 402 are substantially the same as those of steps 201 to 202 in the embodiment shown in fig. 2, and will not be described herein.
Step 403, recognizing the prefix and/or the suffix of the application name in the application name list by using the Chinese language model, and obtaining a recognition result.
In this embodiment, an execution subject (e.g., the server 102 shown in fig. 1) of the method for generating a tag may identify a prefix and/or a suffix of an application name in the application name list to obtain an identification result. In general, the executing body may identify the prefix and/or suffix of the application name in the application name list by using a chinese language model, so as to obtain an identification result. Wherein, N-Gram is a language model commonly used in large vocabulary continuous speech recognition, also called Chinese language model (CLM, chinese Language Model). The Chinese language model can realize automatic conversion to Chinese characters by utilizing collocation information between adjacent words in the context.
In some optional implementations of this embodiment, the prefix and/or suffix of the application name of the part of the application may represent the occupation of the user, where the prefix and/or suffix of the application name of the part of the application is identified, and the obtained identification result may be the occupation of the user. For example, for an application with the application name "х х driver end", the corresponding recognition result may be the driver end. For another example, an application whose application name is "х х doctor edition" may correspond to a doctor as a result of recognition.
Step 404, matching the recognition result in a preset second crowd category set to obtain a second crowd category successfully matched with the recognition result, wherein the second crowd category is used as the crowd category to which the user belongs.
In this embodiment, the executing body may preset the second crowd category set. For example, the executive may generate a second set of crowd categories based on various career types. At this time, the second crowd category set includes various occupation types. Among other sources of professional types may include, but are not limited to, grabbing from professional class technical exam sites. Then, the executing body can match the identification result with each second crowd category set in the second crowd category sets one by one, if the identification result is the same as the occupation type corresponding to one second crowd category, the matching is successful, and the second crowd category is the successfully matched second crowd category, namely the crowd category to which the user belongs.
In some optional implementations of this embodiment, the execution body may further perform statement structure analysis on the statements in the related information first to determine statements that conform to a preset statement structure; and then matching the sentences conforming to the preset sentence structure in a second crowd category set to obtain a second crowd category successfully matched with the sentences conforming to the preset sentence structure, wherein the second crowd category is used as the crowd category to which the user belongs. As an example, the executing body may determine that the sentence structure is "special х х" from the related information, then match each sentence with the sentence structure of "special х х" with each second crowd category in the second crowd category set one by one, and if the sentence with the sentence structure of "special х х" has the same keyword as one second crowd category, the matching is successful, and the second crowd category is the successfully matched second crowd category, that is, the crowd category to which the user belongs. For example, a sentence "dedicated х х" may be "intelligent teaching software developed dedicated to teachers", and the keyword "teacher" in the sentence may be successfully matched with a second crowd category corresponding to "teacher" in the second crowd category set.
Step 405, generating a corresponding crowd category label based on the crowd category to which the user belongs.
In this embodiment, the specific operation of step 405 is substantially the same as that of step 204 in the embodiment shown in fig. 2, and will not be described herein.
As can be seen from fig. 4, compared with the embodiment corresponding to fig. 2, the flow 400 of the method for generating a label in this embodiment highlights the step of matching the recognition result corresponding to the prefix and/or suffix of the application name in the application name list in the second crowd category set, so as to obtain the crowd category to which the user belongs. Therefore, the crowd category of the user is obtained based on the prefix and/or the identification result corresponding to the suffix of the application name, and the accuracy of the determined crowd category is further improved.
With further reference to fig. 5, a flow 500 of another embodiment of a method for generating tags according to the present application is shown. The method for generating a tag comprises the following steps:
step 501, an application name list of an application operated by a user in a history period is obtained.
Step 502, acquiring related information of an application in an application name list.
In this embodiment, the specific operations of steps 501-502 are substantially the same as those of steps 201-202 in the embodiment shown in fig. 2, and will not be described herein.
Step 503, extracting keywords in the related information by using natural language processing technology.
In this embodiment, an execution subject of the method for generating a tag (e.g., the server 102 shown in fig. 1) may extract keywords from the related information. In general, the execution subject may extract keywords in the related information using NLP technology.
And step 504, classifying the application in the name list by taking the application names in the application name list and the keywords in the related information as classification features, and determining the crowd category to which the user belongs based on the classification result.
In this embodiment, the execution body may first classify the application in the name list by using the application name in the application name list and the keyword in the related information as classification features; and then determining the crowd category to which the user belongs based on the classification result. In general, the execution subjects may be classified by a clustering method. Wherein clustering is the process of dividing a collection of physical or abstract objects into classes composed of similar objects. Clusters generated by a cluster are a collection of data objects that are similar to objects in the same cluster, and are different from objects in other clusters. Subsequently, the executing body may extract key information of the applications in the same cluster and determine the crowd category based on the key information.
Step 505, generating a corresponding crowd category label based on the crowd category to which the user belongs.
In this embodiment, the specific operation of step 505 is substantially the same as that of step 204 in the embodiment shown in fig. 2, and will not be described herein.
As can be seen from fig. 5, compared with the corresponding embodiment of fig. 2, the flow 500 of the method for generating labels in this embodiment highlights the step of determining the crowd category to which the user belongs based on the classification result of classifying the applications in the name list based on the classification feature. Therefore, the crowd category of the user is obtained based on keyword matching, and the accuracy of the determined crowd category is further improved.
With further reference to fig. 6, as an implementation of the method shown in the foregoing figures, the present application provides an embodiment of an apparatus for generating a label, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus is particularly applicable to various electronic devices.
As shown in fig. 6, the apparatus 600 for generating a tag of the present embodiment may include: a first acquisition unit 601, a second acquisition unit 602, a determination unit 603, and a generation unit 604. Wherein, the first obtaining unit 601 is configured to obtain an application name list of an application operated by a user in a history period; a second acquiring unit 602 configured to acquire related information of an application in the application name list; a determining unit 603 configured to determine a group category to which the user belongs based on the application name list and the related information; the generating unit 604 is configured to generate a corresponding crowd category label based on the crowd category to which the user belongs.
In the present embodiment, in the apparatus 600 for generating a tag: specific processes and technical effects of the first acquiring unit 601, the second acquiring unit 602, the determining unit 603, and the generating unit 604 may refer to the descriptions related to step 201, step 202, step 203, and step 204 in the corresponding embodiment of fig. 2, and are not repeated herein.
In some optional implementations of the present embodiment, the determining unit 603 is further configured to: extracting keywords in the application name list and related information by using a natural language processing technology; and matching the keywords in the application name list and the related information in a preset first person group category set to obtain a successfully matched first person group category which is used as the group category to which the user belongs.
In some optional implementations of the present embodiment, the determining unit 603 is further configured to: identifying the prefix and/or the suffix of the application name in the application name list by using the Chinese language model to obtain an identification result; and matching the identification result in a preset second crowd category set to obtain a second crowd category successfully matched with the identification result, wherein the second crowd category is used as the crowd category to which the user belongs.
In some optional implementations of the present embodiment, the determining unit 603 is further configured to: carrying out statement structure analysis on the statements in the related information to determine the statements conforming to the preset statement structure; and matching the sentences conforming to the preset sentence structure in a second crowd category set to obtain a second crowd category successfully matched with the sentences conforming to the preset sentence structure, wherein the second crowd category is used as the crowd category to which the user belongs.
In some optional implementations of the present embodiment, the determining unit 603 is further configured to: extracting keywords in the related information by using a natural language processing technology; and classifying the application in the name list by taking the application names in the application name list and the keywords in the related information as classification features, and determining the crowd category to which the user belongs based on the classification result.
Referring now to FIG. 7, there is illustrated a schematic diagram of a computer system 700 suitable for use in implementing an electronic device (e.g., server 102 shown in FIG. 1) of an embodiment of the present application. The electronic device shown in fig. 7 is only an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present application.
As shown in fig. 7, the computer system 700 includes a Central Processing Unit (CPU) 701, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the system 700 are also stored. The CPU 701, ROM 702, and RAM 703 are connected to each other through a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.
The following components are connected to the I/O interface 705: an input section 706 including a keyboard, a mouse, and the like; an output portion 707 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 708 including a hard disk or the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. The drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read therefrom is mounted into the storage section 708 as necessary.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 709, and/or installed from the removable medium 711. The above-described functions defined in the method of the present application are performed when the computer program is executed by a Central Processing Unit (CPU) 701. It should be noted that the computer readable medium described in the present application may be a computer readable signal medium or a computer readable medium, or any combination of the two. The computer readable medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present application may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present application may be implemented by software, or may be implemented by hardware. The described units may also be provided in a processor, for example, described as: a processor includes a first acquisition unit, a second acquisition unit, a determination unit, and a generation unit. The names of these units do not constitute a limitation on the unit itself in some cases, and for example, the first acquisition unit may also be described as "a unit that acquires a list of application names of applications that have been operated by the user during a history period".
As another aspect, the present application also provides a computer-readable medium that may be contained in the electronic device described in the above embodiment; or may exist alone without being incorporated into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring an application name list of an application operated by a user in a historical time period; acquiring related information of an application in an application name list; determining the group category of the user based on the application name list and the related information; and generating a corresponding crowd category label based on the crowd category to which the user belongs.
The foregoing description is only of the preferred embodiments of the present application and is presented as a description of the principles of the technology being utilized. It will be appreciated by persons skilled in the art that the scope of the invention referred to in this application is not limited to the specific combinations of features described above, but it is intended to cover other embodiments in which any combination of features described above or equivalents thereof is possible without departing from the spirit of the invention. Such as the above-described features and technical features having similar functions (but not limited to) disclosed in the present application are replaced with each other.

Claims (10)

1. A method for generating a tag, comprising:
acquiring an application name list of an application operated by a user in a historical time period;
acquiring related information of the application in the application name list;
determining the crowd category of the user based on the application name list and the related information;
generating a corresponding crowd category label based on the crowd category to which the user belongs;
wherein the determining, based on the application name list and the related information, a crowd category to which the user belongs includes:
identifying the prefix and/or the suffix of the application name in the application name list by utilizing a Chinese language model to obtain an identification result;
and matching the identification result in a preset second crowd category set to obtain a second crowd category successfully matched with the identification result as the crowd category to which the user belongs, wherein the second crowd category set comprises various occupation types.
2. The method of claim 1, wherein the determining the group of people to which the user belongs based on the list of application names and the related information comprises:
extracting keywords in the application name list and the related information by using a natural language processing technology;
and matching the application name list with the keywords in the related information in a preset first person group category set to obtain a successfully matched first person group category which is used as the group category to which the user belongs.
3. The method of claim 2, wherein the determining the group of people to which the user belongs based on the list of application names and the related information comprises:
carrying out statement structure analysis on the statements in the related information to determine the statements conforming to a preset statement structure;
and matching the sentences conforming to the preset sentence structure in the second crowd category set to obtain a second crowd category successfully matched with the sentences conforming to the preset sentence structure as the crowd category to which the user belongs.
4. The method of claim 1, wherein the determining the group of people to which the user belongs based on the list of application names and the related information comprises:
extracting keywords in the related information by using a natural language processing technology;
and classifying the application in the name list by taking the application names in the application name list and the keywords in the related information as classification features, and determining the crowd category to which the user belongs based on the classification result.
5. An apparatus for generating a tag, comprising:
a first acquisition unit configured to acquire an application name list of an application operated by a user in a history period;
a second acquisition unit configured to acquire related information of an application in the application name list;
a determining unit configured to determine a group category to which the user belongs based on the application name list and the related information;
a generation unit configured to generate a corresponding crowd category tag based on a crowd category to which the user belongs;
wherein the determining unit is further configured to:
identifying the prefix and/or the suffix of the application name in the application name list by utilizing a Chinese language model to obtain an identification result;
and matching the identification result in a preset second crowd category set to obtain a second crowd category successfully matched with the identification result as the crowd category to which the user belongs, wherein the second crowd category set comprises various occupation types.
6. The apparatus of claim 5, wherein the determination unit is further configured to:
extracting keywords in the application name list and the related information by using a natural language processing technology;
and matching the application name list with the keywords in the related information in a preset first person group category set to obtain a successfully matched first person group category which is used as the group category to which the user belongs.
7. The apparatus of claim 6, wherein the determination unit is further configured to:
carrying out statement structure analysis on the statements in the related information to determine the statements conforming to a preset statement structure;
and matching the sentences conforming to the preset sentence structure in the second crowd category set to obtain a second crowd category successfully matched with the sentences conforming to the preset sentence structure as the crowd category to which the user belongs.
8. The apparatus of claim 5, wherein the determination unit is further configured to:
extracting keywords in the related information by using a natural language processing technology;
and classifying the application in the name list by taking the application names in the application name list and the keywords in the related information as classification features, and determining the crowd category to which the user belongs based on the classification result.
9. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon,
when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-4.
10. A computer readable medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the method of any of claims 1-4.
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