CN110717101B - User classification method and device based on application behaviors and electronic equipment - Google Patents
User classification method and device based on application behaviors and electronic equipment Download PDFInfo
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Abstract
The invention discloses a user classification method based on application behaviors. The method comprises the following steps: acquiring data of user application, wherein the data of the application comprises application attribute data and operation data of a user on the application; extracting and classifying data of the user similar application; constructing an application preference scoring rule; calculating preference scores of the users for each type of application based on the data of the users of the same type of application and the application preference scoring rule; and confirming the user type according to the preference score.
Description
Technical Field
The invention relates to the field of computer information processing, in particular to a user classification method and device based on application behaviors, electronic equipment and a computer readable medium.
Background
With the rapid development of the internet in recent years, users can use terminals such as mobile phones and computers to do things which are related to their preferences such as investment, financing, entertainment or learning, and the like, and the use is simple and convenient. In the current application market, the number of applications is large, the types are different, and the requirements of users in each industry can be basically met.
The user can always reflect the requirements and preferences of the user when downloading and using the application, and the user types can be mastered more accurately by specifically counting and analyzing the user behaviors, so that more accurate wind control and marketing strategies are formulated. This is precisely what is missing in the prior art.
Disclosure of Invention
The embodiment of the specification provides a user classification method and device based on application behaviors, electronic equipment and a computer readable medium, and is used for solving the problem that user application classification is difficult in the prior art.
The application provides a user classification method based on application behaviors, which comprises the following steps:
acquiring data of user application, wherein the data of the application comprises application attribute data and operation data of a user on the application;
extracting and classifying data of the user similar application;
constructing an application preference scoring rule;
calculating preference scores of the users for each type of application based on the data of the users of the same type of application and the application preference scoring rule;
and confirming the user type according to the preference score.
Optionally, comprising:
the extracting and classifying the data of the user similar application comprises the following steps:
extracting characteristic parameters which represent application types and are contained in the applied data based on the applied data, classifying the data of the user similar application according to the characteristic parameters, and determining a primary label corresponding to the data of the user similar application.
Optionally, comprising:
the application preference scoring rule is constructed by the following steps:
acquiring and normalizing the application quantity of the first-level label, the application use period and the time difference of different application installations;
giving preference score weights different in application number, application use period and time difference of different application installation to the primary label;
and acquiring the preference score applied to the primary label by the user based on the preference score weight and the normalized data.
Optionally, the obtaining and normalizing the number of applications of the primary label, the application use period, and the time difference between different application installations includes:
acquiring the application quantity by counting the application downloading quantity in each primary label;
acquiring the application service cycle by calculating the time difference between the earliest installation application distance in each primary label and the application time;
and obtaining the time difference of installation of different applications by calculating the time difference of installation between the earliest and latest installed applications in each primary label.
Optionally, the calculating a preference score of the user for each type of application based on the data of the user's similar applications and the application preference scoring rule includes:
and calculating the score of each primary label according to the application preference scoring rule to serve as the preference score of each type of application.
Optionally, the determining the user type according to the preference score includes:
and determining the user type according to the primary label with the highest score based on the scores of the plurality of primary labels.
Optionally, the user application data further corresponds to a plurality of secondary labels, and the primary label is divided into a plurality of secondary labels.
Optionally, the method further comprises:
and determining the secondary labels corresponding to the user application data of the same type.
The application also provides a user classification device based on the application behaviors, which comprises:
the system comprises a user data module, a data processing module and a data processing module, wherein the user data module is used for acquiring data of user application, and the data of the application comprises application attribute data and operation data of a user on the application;
the data extraction and classification module is used for extracting and classifying the data of the user similar application;
the scoring construction module is used for constructing an application preference scoring rule;
the score calculating module is used for calculating preference scores of the users for each type of application based on the data of the users of the same type of application and the application preference scoring rules;
and the type confirmation module is used for confirming the user type according to the preference score.
Optionally, comprising:
the data extraction and classification module comprises:
and the characteristic parameter extraction and classification unit is used for extracting the characteristic parameters which represent the application types and are contained in the applied data based on the applied data, classifying the data of the user similar application according to the characteristic parameters, and determining a primary label corresponding to the data of the user similar application.
Optionally, comprising:
the score construction module comprises:
the acquisition unit is used for acquiring and normalizing the application number, the application use period and the time difference of different application installation of the primary label;
and the weight giving unit is used for giving preference score weights different in application number, application use period and time difference of different application installation of the primary label.
Optionally, the obtaining unit is configured to obtain and normalize the number of applications of the primary tag, an application usage period, and a time difference between different application installations, and includes:
acquiring the application quantity by counting the application downloading quantity in each primary label;
acquiring the application service cycle by calculating the time difference between the earliest installation application distance in each primary label and the application time;
and obtaining the time difference of installation of different applications by calculating the time difference of installation between the earliest and latest installed applications in each primary label.
Optionally, the score calculating module is configured to calculate a preference score for each type of application based on data of the similar type of application of the user and the application preference scoring rule, and includes:
and calculating the score of each primary label according to the application preference scoring rule to serve as the preference score of each type of application.
Optionally, the type confirmation module is configured to confirm the user type according to the preference score, and includes:
and determining the user type according to the score of the primary labels with the highest score.
Optionally, the user application data further corresponds to a plurality of secondary labels, and the primary label is divided into a plurality of secondary labels.
Optionally, the feature parameter extraction and classification unit is further configured to determine a secondary label corresponding to the same kind of the user application data.
The present application further provides a server comprising a processor and a memory:
the memory is used for storing a program for executing the method of any one of the above methods;
the processor is configured to execute programs stored in the memory.
The present application also provides a computer readable storage medium storing a computer program, wherein the program when executed by a processor implements the steps of any of the methods described above.
The embodiment of the invention extracts and classifies the application data of the same class of users by acquiring the data of the user application, through the application attribute data of the application data and the operation data of the user to the application, constructs the application scoring rule, calculates the preference score of each application of the same class through the application data of the same class and the application scoring rule, and confirms the user type based on the application of the same class with the highest assigned score.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
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In order to make the technical problems solved by the present invention, the technical means adopted and the technical effects obtained more clear, the following will describe in detail the embodiments of the present invention with reference to the accompanying drawings. It should be noted, however, that the drawings described below are only illustrations of exemplary embodiments of the invention, from which other embodiments can be derived by those skilled in the art without inventive faculty.
FIG. 1 is a schematic diagram illustrating a user classification method based on application behaviors according to an embodiment of the present invention;
fig. 2 is a block diagram of a user classification method based on application behaviors according to an embodiment of the present invention;
fig. 3 is a schematic diagram illustrating a principle of a user classification device based on application behaviors according to an embodiment of the present invention;
fig. 4 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will now be described more fully with reference to the accompanying drawings. The exemplary embodiments, however, may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the invention to those skilled in the art. The same reference numerals denote the same or similar elements, components, or portions in the drawings, and thus, a repetitive description thereof will be omitted.
Features, structures, characteristics or other details described in a particular embodiment do not preclude the fact that the features, structures, characteristics or other details may be combined in a suitable manner in one or more other embodiments in accordance with the technical idea of the invention.
In describing particular embodiments, the present invention has been described with reference to features, structures, characteristics or other details that are within the purview of one skilled in the art to provide a thorough understanding of the embodiments. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific features, structures, characteristics, or other details.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various elements, components, or sections, these terms should not be construed as limiting. These phrases are used to distinguish one from another. For example, a first device may also be referred to as a second device without departing from the spirit of the present invention.
The term "and/or" and/or "includes any and all combinations of one or more of the associated listed items.
The invention provides a user classification method based on application behaviors, which has the following general idea:
acquiring data of user application, wherein the data of the application comprises application attribute data and operation data of a user on the application;
extracting and classifying data of the user similar application;
constructing an application preference scoring rule;
calculating preference scores of the users for each type of application based on the data of the users of the same type of application and the application preference scoring rule;
and confirming the user type according to the preference score.
The method of the embodiment extracts and classifies the application data of the same type of the user by acquiring the application attribute data in the application data of the user and the operation data of the user on the application, constructs an application scoring rule for the application data of the user, calculates the preference score of each application of the same type according to the application data of the same type and the application scoring rule, and selects the application of the same type with the highest assigned score as the finally confirmed user type.
The technical solution of the present invention will be described and explained in detail by means of several specific examples.
Referring to fig. 1 and 2, the application behavior-based user classification method includes:
s101: acquiring data of user application, wherein the data of the application comprises application attribute data and operation data of a user on the application;
the type of the data of the user application is obtained by one or more of the following modes:
the method comprises the steps of applying for a user ID, applying for user equipment information, applying for user social behavior data and the like.
S102: extracting and classifying data of the user similar application;
the extracting and classifying the data of the user homogeneous application comprises the following steps:
extracting characteristic parameters which represent application types and are contained in the applied data based on the applied data, classifying the data of the user similar application according to the characteristic parameters, and determining a primary label corresponding to the data of the user similar application.
In an embodiment of the invention, the data of the application may be, in particular, a list of applications of the user's mobile terminal, and the characteristic parameter may comprise a name of the application. The application purpose can be directly defined by the application name. Applications in the whole application list can be classified according to application names, and the applications with the same purpose are of the same class. For example: the application list of the user comprises a plurality of applications for learning English, chinese and mathematics, which can be summarized into a preference knowledge type large class and confirmed as a first-level label. Meanwhile, a plurality of applications such as music applications and video applications are also included in the application list of the user, the applications can be summarized into a major category of the preferred audio-visual entertainment type, and the major category of the preferred audio-visual entertainment type is confirmed as a primary label. Similarly, other types of applications are classified in the above two classification manners, and the applications in the application list are divided into a plurality of first-level labels.
S103: constructing an application preference scoring rule;
the application preference scoring rule construction method comprises the following steps:
acquiring and normalizing the application quantity of the first-level label, the application service cycle and the time difference of different application installations;
giving preference score weights different in application number, application use period and time difference of different application installation to the primary label;
and acquiring the preference score applied to the primary label by the user based on the preference score weight and the normalized data.
The acquiring and normalizing the application quantity, the application use period and the time difference of different application installation of the first-level label comprises the following steps:
acquiring the application quantity by counting the application downloading quantity in each primary label;
acquiring the application service cycle by calculating the time difference between the earliest installation application distance in each primary label and the application time;
and obtaining the time difference of installation of different applications by calculating the time difference of installation between the earliest and latest installed applications in each primary label.
In the embodiment of the invention, the downloading number of the application in each primary label is calculated and counted according to the determined plurality of primary labels, and the score given by the primary label with the larger downloading number of the application is higher. Then, the time difference between the earliest installed application in each primary label and the application time (i.e. the use time of the earliest installed application) is calculated to obtain the application use period, for example: the application use period can be obtained according to the use time of the three applications downloaded first in the same primary label. The higher the score given by the first three downloaded applications using the primary label with the longest cumulative time. And finally, acquiring the time difference of installation of different applications by calculating the time difference of installation between the earliest and latest installed applications in each primary label, wherein the primary label with larger time difference has higher score.
S104: calculating preference scores of the users for each type of application based on the data of the users of the same type of application and the application preference scoring rule;
the calculating the preference score of the user to each type of application based on the data of the user similar type of application and the application preference scoring rule comprises the following steps:
and calculating the score of each primary label according to the application preference scoring rule to serve as the preference score of each type of application.
S105: confirming the user type according to the preference score;
the confirming the user type according to the preference score comprises:
and determining the user type according to the primary label with the highest score based on the scores of the plurality of primary labels.
In the embodiment of the invention, the data of the similar applications of the user and the application preference scoring rules are integrated, each primary label is endowed with a final score, the final score of each primary label is distributed from 0 to 1, the category with the highest score of the primary label of the user is the preference category of the user, and the user type can be confirmed according to the preference category. For example: the value of the first-level label of the user preference knowledge class is 0.98, the value of the first-level label of the preference financing class is 0.80, and the value of the first-level label of the preference consumption class is 0.75, so that the user can be judged as the preference knowledge class. Therefore, different credit and transaction strategies can be formulated according to the preference categories of the customers, the dynamic support rate is higher, the overdue rate is lower, and applicable personalized products can be recommended to the users.
The user application data also corresponds to a plurality of secondary labels, and the primary labels are divided into a plurality of secondary labels. And after the user type is confirmed, determining the secondary labels corresponding to the user application data of the same type, and further refining the category of the user application. For example: when the customer is a preference knowledge class, further refinement can be made to the application within the primary label. For example, the primary label contains a child education application, a classical literature application and a foreign literature application, and the child education application, the classical literature application and the foreign literature application are all secondary labels under the primary label. And the number of children education applications is the largest, it can be determined that the user prefers children education. And whether children exist or not can be judged according to the preference of the user, the general age of the children can be deduced, and therefore a proper financial product is recommended to the user, and the success rate of product recommendation can be improved.
Those skilled in the art will appreciate that all or part of the steps to implement the above-described embodiments are implemented as programs (computer programs) executed by a computer data processing apparatus. When the computer program is executed, the method provided by the invention can be realized. Furthermore, the computer program may be stored in a computer readable storage medium, which may be a readable storage medium such as a magnetic disk, an optical disk, a ROM, a RAM, or a storage array composed of a plurality of storage media, such as a magnetic disk or a magnetic tape storage array. The storage medium is not limited to centralized storage, but may be distributed storage, such as cloud storage based on cloud computing.
Embodiments of the apparatus of the present invention are described below, which may be used to perform method embodiments of the present invention. The details described in the device embodiments of the invention should be regarded as complementary to the above-described method embodiments; reference is made to the above-described method embodiments for details not disclosed in the embodiments of the inventive device.
Based on the same inventive concept as the user classification method based on the application behaviors in the embodiment, the invention also provides a user classification device based on the application behaviors.
Referring to fig. 3, the apparatus for classifying a user based on application behavior includes:
a user data module 301, configured to obtain data of a user application, where the data of the application includes application attribute data and operation data of the application by a user;
the method comprises the steps of applying for a user ID, applying for user equipment information, applying for user social behavior data and the like.
A data extraction and classification module 302, configured to extract and classify data of the user homogeneous application;
specifically, the data extraction classification module 302 includes:
and the characteristic parameter extraction and classification unit is used for extracting the characteristic parameters which represent the application types and are contained in the applied data based on the applied data, classifying the data of the user similar application according to the characteristic parameters, and determining a primary label corresponding to the data of the user similar application.
A score construction module 303, configured to construct an application preference score rule;
specifically, score construction module 303 includes:
the acquisition unit is used for acquiring and normalizing the application quantity of the first-level label, the application service cycle and the time difference of different application installations; the application quantity can be obtained by counting the application download quantity in each primary label. The application use period can be obtained by calculating the time difference between the earliest installed application in each primary label and the application time. The time difference of installation of the different applications can be obtained by calculating the time difference of installation between the earliest and latest installed applications in each of the primary labels.
And the weight giving unit is used for giving preference score weights different in application number, application use cycle and time difference of different application installation of the primary label.
A score calculating module 304, configured to calculate a preference score for each type of application by the user based on data of the similar type of application by the user and the application preference scoring rule;
specifically, the score calculating module 304 includes:
and calculating the score of each primary label according to the application preference scoring rule to serve as the preference score of each type of application.
A type confirmation module 305 for confirming the user type according to the preference score;
specifically, the type confirmation module 305 includes:
and determining the user type according to the score of the primary labels with the highest score.
The user application data also corresponds to a plurality of secondary labels, and the primary labels are divided into a plurality of secondary labels.
The characteristic parameter extracting and classifying unit is further used for determining the secondary labels corresponding to the same kind of the user application data.
Those skilled in the art will appreciate that the modules in the above-described embodiments of the apparatus may be distributed as described in the apparatus, and that corresponding variations may be made in one or more apparatus other than the above-described embodiments. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
In the following, embodiments of the electronic device of the present invention are described, which may be regarded as specific physical implementations for the above-described embodiments of the method and apparatus of the present invention. The details described in the embodiments of the electronic device of the invention are to be regarded as supplementary for the embodiments of the method or the apparatus described above; for details not disclosed in the embodiments of the electronic device of the present invention, reference may be made to the above-described embodiments of the method or apparatus.
Fig. 4 is a block diagram of an electronic device according to an embodiment of the present invention. An electronic device 400 provided in accordance with the present invention is described below with reference to fig. 4. The electronic device 400 shown in fig. 4 is only an example and should not bring any limitation to the function and the scope of use of the embodiments of the present invention.
As shown in fig. 4, electronic device 400 is embodied in the form of a general purpose computing device. The components of electronic device 400 may include, but are not limited to: at least one processing unit 410, at least one memory unit 420, a bus 430 that connects the various system components (including the memory unit 420 and the processing unit 410), a display unit 440, and the like.
Wherein the storage unit stores program code executable by the processing unit 410 to cause the processing unit 410 to perform the steps according to various exemplary embodiments of the present invention described in the above-mentioned electronic prescription flow processing method section of the present specification. For example, the processing unit 410 may perform the steps shown in fig. 4.
The memory unit 420 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM) 4201 and/or a cache memory unit 4202, and may further include a read only memory unit (ROM) 4203.
The storage unit 420 may also include a program/utility 4204 having a set (at least one) of program modules 4205, such program modules 4205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination thereof may comprise an implementation of a network environment.
The electronic device 400 may also communicate with one or more external devices 500 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 400, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 400 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 450. Also, the electronic device 400 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) through the network adapter 460. The network adapter 460 may communicate with other modules of the electronic device 400 via the bus 430. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments of the present invention described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to make a computing device (which can be a personal computer, a server, or a network device, etc.) execute the above-mentioned method according to the present invention. When executed by a data processing device, the computer program enables the computer readable medium to implement the above method of the present invention, namely: the user classification method based on the application behaviors comprises the following steps: acquiring data of user application, wherein the data of the application comprises application attribute data and operation data of a user on the application; extracting and classifying data of the user similar application; constructing an application preference scoring rule; calculating preference scores of the users for each type of application based on the data of the users of the same type of application and the application preference scoring rule; and confirming the user type according to the preference score. The embodiment of the invention extracts and classifies the application data of the same class of users by acquiring the data of the user application, through the application attribute data of the application data and the operation data of the user to the application, constructs the application scoring rule, calculates the preference score of each application of the same class through the application data of the same class and the application scoring rule, and confirms the user type based on the application of the same class with the highest assigned score.
The computer program may be stored on one or more computer readable media. The computer readable medium may be a readable signal medium or a readable storage medium. A readable storage 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 (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, 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.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. 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 thereof. A readable storage medium may also be any readable medium that is not a readable storage 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 readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, 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 computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
In summary, the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functionality of some or all of the components in embodiments in accordance with the invention may be implemented in practice using a general purpose data processing device such as a microprocessor or a Digital Signal Processor (DSP). The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
While the foregoing detailed description has described in detail certain embodiments of the invention with reference to certain specific aspects, embodiments and advantages thereof, it should be understood that the invention is not limited to any particular computer, virtual machine, or electronic device, as various general purpose machines may implement the invention. The present invention is not limited to the above embodiments, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The user classification method based on the application behaviors is characterized by comprising the following steps:
acquiring data of user application, wherein the data of the application comprises application attribute data and operation data of a user on the application;
extracting characteristic parameters containing application names in the application data based on the application data, classifying the data of the user similar applications according to the application names in the characteristic parameters, and determining a primary label corresponding to the data of the user similar applications;
constructing an application preference scoring rule;
acquiring the application quantity by counting the application download number in each primary label, acquiring the application use period by calculating the time difference between the earliest installed application in each primary label and the application time, acquiring the time difference between different application installations by calculating the time difference between the earliest installed application and the latest installed application in each primary label, giving different preference score weights to the application quantity, the application use period and the time difference between different application installations of the primary labels, and acquiring the preference score of a user on the primary label application based on the preference score weights and the normalized data;
and confirming the user type according to the preference score.
2. The application behavior-based user classification method according to claim 1, wherein the identifying a user type according to the preference score comprises:
and determining the user type according to the primary label with the highest score based on the scores of the plurality of primary labels.
3. The application behavior-based user classification method according to claim 1, characterized in that the user application data further corresponds to a plurality of secondary labels, and the primary labels are divided into a plurality of secondary labels.
4. The application behavior-based user classification method according to claim 3, further comprising:
and determining a secondary label corresponding to the user application data in the same class.
5. User classification device based on application behavior, characterized by comprising:
the system comprises a user data module, a data processing module and a data processing module, wherein the user data module is used for acquiring data of user application, and the data of the application comprises application attribute data and operation data of a user on the application;
the data extraction and classification module is used for extracting and classifying data of the similar applications of the users;
the score construction module is used for constructing an application preference score rule;
the score calculating module is used for calculating the preference score of the user for each type of application based on the data of the user similar type of application and the application preference scoring rule;
the type confirmation module is used for confirming the user type according to the preference score;
wherein, the data extraction and classification module comprises:
the characteristic parameter extracting and classifying unit is used for extracting the characteristic parameters containing application names in the application data based on the application data, classifying the data of the user similar application according to the application names in the characteristic parameters, and determining a primary label corresponding to the data of the user similar application;
the scoring construction module comprises:
the acquisition unit is used for acquiring the application quantity by counting the application downloading number in each primary label, acquiring the application use cycle by calculating the time difference between the earliest installed application in each primary label and the application, and acquiring the time difference of different application installations by calculating the time difference between the earliest installed application and the latest installed application in each primary label;
and the weight giving unit is used for giving preference score weights different in application number, application use period and time difference of different application installation of the primary label.
6. The apparatus as claimed in claim 5, wherein the type identification module is configured to identify the user type according to the preference score, and comprises:
and determining the user type according to the primary label with the highest score based on the scores of the plurality of primary labels.
7. The apparatus according to claim 5, wherein the user application data further corresponds to a plurality of secondary labels, and the primary labels are divided into a plurality of secondary labels.
8. The apparatus according to claim 7, wherein the feature parameter extraction and classification unit is further configured to determine secondary labels corresponding to the user application data of the same type.
9. A server, comprising a processor and a memory, characterized in that:
the memory is used for storing a program for executing the method of any one of claims 1-4;
the processor is configured to execute programs stored in the memory.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 4.
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