CN111292114A - Method and apparatus for generating labels - Google Patents
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- CN111292114A CN111292114A CN201811504653.8A CN201811504653A CN111292114A CN 111292114 A CN111292114 A CN 111292114A CN 201811504653 A CN201811504653 A CN 201811504653A CN 111292114 A CN111292114 A CN 111292114A
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Abstract
The embodiment of the application discloses a method and a device for generating a label. One specific implementation of the method comprises the steps of obtaining an application name list of applications operated by a user in a historical time period; acquiring relevant information of the application in the application name list; determining a crowd category to which the user belongs 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. According to the embodiment, the crowd category to which the user belongs is determined based on the application operated by the user, so that the accuracy of the determined crowd category is improved, and the depicting accuracy and the refining degree of the user attribute are improved.
Description
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to a method and a device for generating a label.
Background
The user representation may be user information including a descriptive label of at least one user attribute established from pre-accumulated multi-source user data. Descriptive tags 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 for various dimensions in the user representation may mine the user's points of interest including, but not limited to, travel, games, sports ball games, and the like. However, the conventional user data for establishing the user profile has certain difficulty, and the authenticity and accuracy of the user data cannot be evaluated, so that the characterization of the user attribute is not accurate enough and not detailed enough.
Disclosure of Invention
The embodiment of the application provides a method and a device for generating a label.
In a first aspect, an embodiment of the present application provides a method for generating a tag, including: acquiring an application name list of applications operated by a user in a historical time period; acquiring relevant information of the application in the application name list; determining a crowd category to which the user belongs 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.
In some embodiments, determining the category 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 keywords in the application name list and the related information in a preset first crowd category set to obtain a successfully matched first crowd category which is used as the crowd category to which the user belongs.
In some embodiments, determining the category of people to which the user belongs based on the list of application names and the related information comprises: identifying prefixes and/or suffixes of application names in the application name list by utilizing the Chinese language model to obtain an identification result; and matching the recognition result in a preset second crowd category set to obtain a second crowd category successfully matched with the recognition result as the crowd category to which the user belongs.
In some embodiments, determining the category of people to which the user belongs based on the list of application names and the related information comprises: carrying out sentence structure analysis on the sentences in the related information to determine the sentences which accord with the preset sentence 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 as the crowd category to which the user belongs.
In some embodiments, determining the category 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 applications 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 applications operated by a user within a history period; a second acquisition unit configured to acquire related information of the application in the application name list; a determination unit configured to determine a crowd category to which the user belongs based on the application name list and the related information; a generating unit configured to generate a corresponding crowd category label based on a 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 the 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 crowd category set to obtain a successfully matched first crowd category which is used as the crowd category to which the user belongs.
In some embodiments, the determining unit is further configured to: identifying prefixes and/or suffixes of application names in the application name list by utilizing the Chinese language model to obtain an identification result; and matching the recognition result in a preset second crowd category set to obtain a second crowd category successfully matched with the recognition result as the crowd category to which the user belongs.
In some embodiments, the determining unit is further configured to: carrying out sentence structure analysis on the sentences in the related information to determine the sentences which accord with the preset sentence 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 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 applications 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; when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method as described in any implementation of the first aspect.
In a fourth aspect, the present application provides a computer-readable medium, on which a computer program is stored, which, when executed by a processor, implements the method as described in any implementation manner of the first aspect.
According to the method and the device for generating the label, firstly, an application name list of the application operated by a user in a historical time period is obtained; then acquiring relevant 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 to which the user belongs is determined based on the application operated by the user, so that the accuracy of the determined crowd category is improved, and the depicting accuracy and the refining degree of the user attribute are improved.
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Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is an exemplary system architecture to which the present application may be applied;
FIG. 2 is a flow diagram of one embodiment of a method for generating labels according to the present application;
FIG. 3 is a flow diagram of yet another embodiment of a method for generating labels in accordance with the present application;
FIG. 4 is a flow diagram of yet another embodiment of a method for generating labels in accordance with the present application;
FIG. 5 is a flow diagram of another embodiment of a method for generating labels in accordance with the present application;
FIG. 6 is a schematic block diagram of one embodiment of an apparatus for generating labels in accordance with the present application;
FIG. 7 is a block diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 shows an exemplary system architecture 100 to which embodiments of the method for generating a tag or the apparatus for generating a tag 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. Network 103 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The server 102 may provide various services, for example, the server 102 may analyze and perform processing on data such as an application name list of applications operated by the user in a historical period acquired from the server 101, and generate a processing result (e.g., 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 implemented as a distributed server cluster composed of multiple servers, or may be implemented as a single server. When the servers 101, 102 are software, they may be implemented as multiple pieces of software or software modules (e.g., to provide distributed services), or as a single piece of software or software module. And is not particularly limited herein.
It should be noted that the method for generating the tag provided by the embodiment of the present application is generally performed by the server 102, and accordingly, the apparatus for generating the 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 a tag in accordance with the present application is shown. The method for generating the label comprises the following steps:
In this embodiment, an execution subject (for example, the server 102 shown in fig. 1) of the method for generating a tag may obtain, through a wired connection manner or a wireless connection manner, an application name list of applications that have been operated by a user in a historical time period (for example, in the previous three months) from a background server (for example, the server 101 shown in fig. 1) of an application market or a cell phone assistant. The applications operated by the user may include, but are not limited to, applications downloaded by the user, applications installed by the user, applications used by the user, applications updated by the user, applications uninstalled by the user, and the like. 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. Generally, 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 may obtain the application name list of the applications operated by the user in the historical time period from the application market or the background server of the mobile phone assistant.
In this embodiment, the execution main body may obtain the relevant information of the application in the application name list from a background server of an application market or a mobile phone assistant, a background server of an encyclopedia application, or a background server of a search application in a wired connection manner or a wireless connection manner. The related information of the application may include, but is not limited to, introduction information of the application, evaluation information of the application by the user, and the like. Generally, the background server of the application market or the mobile phone assistant may further store introduction information of the application and evaluation information of the user on the application, so that the execution subject may obtain the application name list from the background server of the application market or the mobile phone assistant, and also obtain the introduction information of the application and the evaluation information of the user on the application in the application name list. The execution main body may also search, based on the application name in the application name list, a background server of the encyclopedic application or a background server of the search application, so as to obtain the relevant information of the application in the application name list.
And 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 subject may analyze the application name list and the related information to determine the crowd category to which the user belongs. For example, if the application is determined to be a cloud service application of a certain brand of automobile according to the application name and related information of the application, the user belongs to the owner of the brand of automobile. For another example, if the application is determined to be a driver-side application based on the application name and related information of the application, the user belongs to a driver.
And step 204, generating corresponding crowd category labels based on the crowd categories to which the users belong.
In this embodiment, the execution subject may generate a corresponding crowd category label based on the crowd category to which the user belongs.
In some optional implementations of the embodiment, the execution subject may generate the user portrait of the user based on the crowd category tag of the user, so as to improve accuracy of depicting the user portrait on the attribute of the user. The execution subject may then determine the interest points of the user based on the representation of the user, thereby improving the accuracy of the determined interest points. Finally, the execution main body can also push information for the user according to the interest point of the user, so that the accuracy of information pushing is improved.
According to the method for generating the label, firstly, an application name list of an application operated by a user in a historical time period is obtained; then acquiring relevant 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 to which the user belongs is determined based on the application operated by the user, so that the accuracy of the determined crowd category is improved, and the depicting accuracy and the refining degree of the user attribute are improved.
With further reference to fig. 3, a flow 300 of yet another embodiment of a method for generating a tag in accordance with the present application is shown. The method for generating the label comprises the following steps:
In the present embodiment, the specific operations of step 301-.
In the present embodiment, an execution subject (e.g., the server 102 shown in fig. 1) of the method for generating a tag may extract a keyword from the application name list and the related information. In general, the execution agent may extract keywords in the application name list and the related information using NLP (natural language Processing) technology. NLP, among other things, is a way for computers to analyze, understand and capture 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 summarization, translation, named entity recognition, relationship extraction, emotion analysis, speech recognition, and topic segmentation.
And 304, matching the keywords in the application name list and the related information in a preset first crowd category set to obtain a successfully matched first crowd category as the crowd category to which the user belongs.
In this embodiment, the execution subject may preset the first crowd category set. For example, the executing entity may generate the first set of crowd categories based on various automobile brands. At this time, the first group category set includes categories of owners of vehicles of various brands. Then, the execution subject may match the keywords in the application name list and the related information with each first group category set in the first group category set one by one, and if the keywords in the application name list and the related information are the same as the automobile brand corresponding to one first group category, the matching is successful, and the first group category is the successfully matched first group category, which may be the group category to which the user belongs.
In this embodiment, the specific operation of step 305 is substantially the same as the operation of step 204 in the embodiment shown in fig. 2, and is not repeated herein.
As can be seen from fig. 3, compared with the embodiment corresponding to fig. 2, the flow 300 of the method for generating a tag in this embodiment highlights the step of matching the keywords in the application name list and the related information in the first crowd category set to obtain the crowd category to which the user belongs. Therefore, the crowd category to which the user belongs is obtained based on the 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 labels in accordance with the present application is illustrated. The method for generating the label comprises the following steps:
In the present embodiment, the specific operations of steps 401-402 are substantially the same as the operations of steps 201-202 in the embodiment shown in fig. 2, and are not repeated herein.
And 403, identifying the prefixes and/or suffixes of the application names in the application name list by using the Chinese language model to obtain an identification result.
In the present 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. Generally, the execution agent may identify prefixes and/or suffixes of application names in the application name list using a chinese language model to obtain an identification result. The N-Gram is a Language Model commonly used in large vocabulary continuous speech recognition, and is also called a Chinese Language Model (CLM). The Chinese language model can realize automatic conversion to Chinese characters by using collocation information between adjacent words in the context.
In some optional implementation manners of this embodiment, the prefix and/or the suffix of the application name of the part of applications may reflect the occupation of the user, at this time, the prefix and/or the suffix of the application name of the part of applications are identified, and the obtained identification result may be the occupation of the user. For example, for an application with an application name of "х х driver end of the driver generation", the corresponding recognition result may be the driver generation. For another example, for an application with an application name of "х х doctor version", the corresponding recognition result may be a doctor.
And 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 as the crowd category to which the user belongs.
In this embodiment, the execution subject may preset the second crowd category set. For example, the executive may generate the second set of crowd categories based on various types of professions. At this point, various types of professions are included in the second set of demographic categories. Wherein the source of the occupation type may include, but is not limited to, being captured from a professional-type technical examination site. Then, the executing body may match the recognition result with each of the second crowd category sets one by one, and if the recognition 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, which may be the crowd category to which the user belongs.
In some optional implementation manners of this embodiment, the execution main body may further perform statement structure analysis on the statements in the related information to determine statements that conform to a preset statement structure; and matching the sentences conforming to the preset sentence structures in a second crowd category set to obtain a second crowd category successfully matched with the sentences conforming to the preset sentence structures and serve as the crowd category to which the user belongs. As an example, the executing entity may determine a sentence with a sentence structure of "special х х" from the related information, and then match the sentence with the sentence structure of "special х х" with each second crowd category in the second crowd category set one by one, if there is a keyword identical to one second crowd category in the sentence with the sentence structure of "special х х", 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 statement "exclusively х х" may be "intelligent teaching software developed exclusively for teachers," and the keyword "teacher" in the statement may be successfully matched with a second crowd category corresponding to "teacher" in the second crowd category set.
In this embodiment, the specific operation of step 405 is substantially the same as the operation of step 204 in the embodiment shown in fig. 2, and is not described herein again.
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 a step of matching the recognition results corresponding to the prefixes and/or suffixes of the application names in the application name list in the second crowd category set to obtain the crowd category to which the user belongs. Therefore, the crowd category to which the user belongs is obtained based on the matching of the identification results corresponding to the prefix and/or 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 a tag in accordance with the present application is shown. The method for generating the label comprises the following steps:
In the present embodiment, the specific operations of steps 501-502 are substantially the same as the operations of steps 201-202 in the embodiment shown in fig. 2, and are not repeated herein.
In the present embodiment, an execution subject (e.g., the server 102 shown in fig. 1) of the method for generating a tag may extract a keyword from the related information. In general, the execution subject may extract keywords in the related information using NLP technology.
In this embodiment, the execution subject may first classify the applications in the name list by using the application names in the application name list and the keywords in the related information as classification features; and then determining the crowd category to which the user belongs based on the classification result. Generally, the execution subjects may be classified using a clustering method. Clustering is the process of dividing a collection of physical or abstract objects into classes composed of similar objects. The cluster generated by clustering is a collection of a set of data objects that are similar to objects in the same cluster and distinct from objects in other clusters. Subsequently, the execution subject may extract key information of applications in the same cluster, and determine the crowd category based on the key information.
And 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 the operation of step 204 in the embodiment shown in fig. 2, and is not repeated herein.
As can be seen from fig. 5, compared with the embodiment corresponding to fig. 2, the flow 500 of the method for generating labels in the present 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 features. Therefore, the crowd category to which the user belongs is obtained based on the 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 above figures, the present application provides an embodiment of an apparatus for generating a tag, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be applied 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. The first obtaining unit 601 is configured to obtain an application name list of applications operated by a user in a history time period; a second obtaining unit 602 configured to obtain relevant information of the application in the application name list; a determining unit 603 configured to determine a crowd category to which the user belongs based on the application name list and the related information; a generating unit 604 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: the specific processing of the first obtaining unit 601, the second obtaining unit 602, the determining unit 603, and the generating unit 604 and the technical effects thereof can refer to the related descriptions of step 201, step 202, step 203, and step 204 in the corresponding embodiment of fig. 2, which are not described herein again.
In some optional implementations of this embodiment, the determining unit 603 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 keywords in the application name list and the related information in a preset first crowd category set to obtain a successfully matched first crowd category which is used as the crowd category to which the user belongs.
In some optional implementations of this embodiment, the determining unit 603 is further configured to: identifying prefixes and/or suffixes of application names in the application name list by utilizing the Chinese language model to obtain an identification result; and matching the recognition result in a preset second crowd category set to obtain a second crowd category successfully matched with the recognition result as the crowd category to which the user belongs.
In some optional implementations of this embodiment, the determining unit 603 is further configured to: carrying out sentence structure analysis on the sentences in the related information to determine the sentences which accord with the preset sentence 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 as the crowd category to which the user belongs.
In some optional implementations of this 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 applications 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, a block 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 is shown. The electronic device shown in fig. 7 is only an example, and should not bring any limitation to the functions and the 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 in accordance with 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 necessary for the operation of the system 700 are also stored. The CPU 701, the ROM 702, and the RAM 703 are connected to each other via 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 portion 706 including a keyboard, a mouse, and the like; an output section 707 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 708 including a hard disk and 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. A 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 out therefrom is mounted into the storage section 708 as necessary.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the 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 illustrated in the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 709, and/or installed from the removable medium 711. The computer program, when executed by a Central Processing Unit (CPU)701, performs the above-described functions defined in the method of the present application. It should be noted that the computer readable medium described herein can be a computer readable signal medium or a computer readable medium or any combination of the two. A computer readable medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination 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 present application, 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 this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. 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 for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like 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 type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart 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 described in the embodiments of the present application may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes a first acquisition unit, a second acquisition unit, a determination unit, and a generation unit. Where the names of these units do not constitute a limitation on the unit itself in some cases, for example, the first acquisition unit may also be described as a "unit that acquires an application name list of applications that the user has operated within a history period".
As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled 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 applications operated by a user in a historical time period; acquiring relevant information of the application in the application name list; determining a crowd category to which the user belongs 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.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the invention. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (12)
1. A method for generating a tag, comprising:
acquiring an application name list of applications operated by a user in a historical time period;
acquiring relevant information of the application in the application name list;
determining a crowd category to which the user belongs 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.
2. The method of claim 1, wherein said determining a demographic category to which the user belongs based on the list of application names and the relevant 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 and the keywords in the related information in a preset first crowd category set to obtain a successfully matched first crowd category which is used as the crowd category to which the user belongs.
3. The method of claim 1, wherein said determining a demographic category to which the user belongs based on the list of application names and the relevant information comprises:
recognizing prefixes and/or suffixes of the application names in the application name list by utilizing a Chinese language model to obtain a recognition result;
and 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.
4. The method of claim 3, wherein the determining the demographic category to which the user belongs based on the list of application names and the related information comprises:
carrying out sentence structure analysis on the sentences in the related information to determine the sentences which accord with a preset sentence structure;
and matching the sentences conforming to the preset sentence structures in the second crowd category set to obtain a second crowd category successfully matched with the sentences conforming to the preset sentence structures, and taking the second crowd category as the crowd category to which the user belongs.
5. The method of claim 1, wherein said determining a demographic category to which the user belongs based on the list of application names and the relevant information comprises:
extracting keywords in the related information by using a natural language processing technology;
and classifying the applications 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.
6. An apparatus for generating a label, comprising:
a first acquisition unit configured to acquire an application name list of applications operated by a user within 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 crowd category to which the user belongs based on the application name list and the related information;
a generating unit configured to generate a corresponding crowd category label based on a crowd category to which the user belongs.
7. The apparatus of claim 6, 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 and the keywords in the related information in a preset first crowd category set to obtain a successfully matched first crowd category which is used as the crowd category to which the user belongs.
8. The apparatus of claim 6, wherein the determination unit is further configured to:
recognizing prefixes and/or suffixes of the application names in the application name list by utilizing a Chinese language model to obtain a recognition result;
and 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.
9. The apparatus of claim 8, wherein the determination unit is further configured to:
carrying out sentence structure analysis on the sentences in the related information to determine the sentences which accord with a preset sentence structure;
and matching the sentences conforming to the preset sentence structures in the second crowd category set to obtain a second crowd category successfully matched with the sentences conforming to the preset sentence structures, and taking the second crowd category as the crowd category to which the user belongs.
10. The apparatus of claim 6, wherein the determination unit is further configured to:
extracting keywords in the related information by using a natural language processing technology;
and classifying the applications 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.
11. 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, cause the one or more processors to implement the method of any one of claims 1-5.
12. A computer-readable medium, on which a computer program is stored, wherein the computer program, when being executed by a processor, carries out the method according to any one of claims 1-5.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130205260A1 (en) * | 2012-02-02 | 2013-08-08 | Samsung Electronics Co., Ltd | Method and apparatus for managing an application in a mobile electronic device |
| CN104933049A (en) * | 2014-03-17 | 2015-09-23 | 华为技术有限公司 | Method and system for generating digital human |
| US20150363289A1 (en) * | 2012-11-11 | 2015-12-17 | Michael Brough | Mobile device application monitoring software |
| US9223586B1 (en) * | 2013-06-27 | 2015-12-29 | Amazon Technologies, Inc. | Run-time limitations of software applications based on user characteristics |
| CN108429816A (en) * | 2018-03-27 | 2018-08-21 | 百度在线网络技术（北京）有限公司 | Method and apparatus for generating information |
| CN108563680A (en) * | 2018-03-07 | 2018-09-21 | 阿里巴巴集团控股有限公司 | Resource recommendation method and device |
-
2018
- 2018-12-10 CN CN201811504653.8A patent/CN111292114B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130205260A1 (en) * | 2012-02-02 | 2013-08-08 | Samsung Electronics Co., Ltd | Method and apparatus for managing an application in a mobile electronic device |
| US20150363289A1 (en) * | 2012-11-11 | 2015-12-17 | Michael Brough | Mobile device application monitoring software |
| US9223586B1 (en) * | 2013-06-27 | 2015-12-29 | Amazon Technologies, Inc. | Run-time limitations of software applications based on user characteristics |
| CN104933049A (en) * | 2014-03-17 | 2015-09-23 | 华为技术有限公司 | Method and system for generating digital human |
| CN108563680A (en) * | 2018-03-07 | 2018-09-21 | 阿里巴巴集团控股有限公司 | Resource recommendation method and device |
| CN108429816A (en) * | 2018-03-27 | 2018-08-21 | 百度在线网络技术（北京）有限公司 | Method and apparatus for generating information |
Non-Patent Citations (3)
| Title |
|---|
| CHIRAZ EL HOG,等: "Towards an UML Based Modeling Language to Design Adaptive Web Services", pages 38 - 44 * |
| 技术干货: "电商大数据应用之用户画像" * |
| 郭伟，等: "支持ＳａａＳ应用多维异构性能需求的云资源放置方法", vol. 41, no. 6, pages 1005 - 1017 * |
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