CN114091585A - User age estimation method, device and storage medium - Google Patents
User age estimation method, device and storage medium Download PDFInfo
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Abstract
The application discloses a user age group presumption method, a device and a storage medium, which are used for presuming the age of a user according to installed application software. The method comprises the following steps: reading application data of installed application software of a user; generating a corresponding list text according to the application data; screening the application software according to the use time point, the use duration and the use frequency of the application software recorded in the list text to obtain a screened list text; inputting the screened list text serving as a speculative data set into a pre-selected trained random forest classifier, wherein the random forest classifier is obtained by training a sample data set, and the sample data set comprises a plurality of list texts carrying application software of user ages; and outputting a result of conjecture of the user age of the user through the random forest classifier.
Description
Technical Field
The present application relates to the field of data processing, and in particular, to a method and an apparatus for estimating a user age, and a storage medium.
Background
With the popularization and development of the mobile internet and the mobile internet, developers and operators of application software are more and more concerned about information of current main use groups of products, such as age group distribution, gender, use habits and the like of the groups, so as to develop functions of the products more specifically in a subsequent stage, and provide more accurate goods or services for customers.
In practice, users of different ages use intelligent devices such as computers and mobile phones, and preferences of application software are different, when a user makes a transaction on the application software or the user registers after downloading the application software, the application software requires the user to input registration information, and in the prior art, the user's age is generally known through the registration information.
Disclosure of Invention
In order to solve the above technical problem, the present application provides a user age estimation method, apparatus, and storage medium.
A first aspect of the present application provides a method for estimating a user age, the method including:
reading application data of installed application software of a user;
generating a corresponding list text according to the application data;
screening the application software according to the use time point, the use duration and the use frequency of the application software recorded in the list text to obtain a screened list text;
inputting the screened list text serving as a speculative data set into a pre-selected trained random forest classifier, wherein the random forest classifier is obtained by training a sample data set, and the sample data set comprises a plurality of list texts carrying application software of user ages;
and outputting a result of conjecture of the user age of the user through the random forest classifier.
Optionally, the screening the application software according to the usage time point, the usage duration, and the usage frequency of the application software recorded in the list text includes:
removing application data corresponding to unused application software exceeding a preset time point from the list text;
removing application data corresponding to the application software with the use duration not reaching the preset duration from the list text;
and removing application data corresponding to the application software of which the use frequency does not reach the preset frequency from the list text.
Optionally, the application data includes description information of the application software, and the generating a corresponding list text according to the application data includes:
and extracting the keywords in the description information and recording the keywords in a list text.
Optionally, the description information is information obtained according to release information of the application software in the application market.
Optionally, the random forest classifier is obtained by training according to the following method:
acquiring a first sample data set, inputting the first sample data set into an initialized random forest classifier, and training the initialized random forest classifier, wherein the first sample data set comprises a list text of application software carrying the age of a user;
inputting the second sample data set into a trained random forest classifier, conjecturing the age of the user, and determining a third sample data set with a confidence coefficient reaching a preset confidence coefficient threshold;
setting the confidence of the third sample data to be highest;
and inputting the third sample data set and the second sample data set into the trained random forest classifier again for training until the random forest classifier is converged to obtain the random forest classifier.
A second aspect of the present application provides a user age estimation apparatus, the apparatus including:
a reading unit for reading application data of installed application software of a user;
the generating unit is used for generating a corresponding list text according to the application data;
the screening unit is used for screening the application software according to the use time point, the use duration and the use frequency of the application software recorded in the list text to obtain a screened list text;
the input unit is used for inputting the screened list texts serving as a speculative data set into a pre-selected trained random forest classifier, wherein the random forest classifier is obtained by training a sample data set, and the sample data set comprises a plurality of list texts carrying application software of the user age;
and the output unit is used for outputting the estimation result of the user age of the user through the random forest classifier.
Optionally, the screening unit is specifically configured to:
removing application data corresponding to unused application software exceeding a preset time point from the list text;
removing application data corresponding to the application software with the use duration not reaching the preset duration from the list text;
and removing application data corresponding to the application software of which the use frequency does not reach the preset frequency from the list text.
Optionally, the generating unit is specifically configured to:
and extracting the keywords in the description information and recording the keywords in a list text.
A third aspect of the present application provides a user age estimation apparatus, including:
the device comprises a processor, a memory, an input and output unit and a bus;
the processor is connected with the memory, the input and output unit and the bus;
the memory holds a program that the processor calls to perform the method of any of the first aspect and the first aspect.
A fourth aspect of the present application provides a computer readable storage medium having a program stored thereon, which when executed on a computer performs the method of any one of the first aspect and the first aspect.
According to the technical scheme, the method has the following advantages:
the method provided by the application can be used for conjecturing the age of the user, reading the application data of the installed application software of the user during conjecture, generating a corresponding list text according to the application data, screening the application software according to the using time point, the using duration and the using frequency of the application software recorded in the list text to obtain the screened list text, and effectively improving the validity of conjecture data by screening the list text, thereby bringing the accuracy to the final conjecture result, inputting the screened list text into a pre-selected trained random forest classifier as a conjecture data set, outputting the conjecture result of the age of the user by the random forest classifier, conjecturing the age of the user by the random forest classifier, having higher accuracy, and being independent of the registration information or transaction information of the user and the like, the method has certain general applicability and can meet a plurality of use scenes.
Drawings
In order to more clearly illustrate the technical solutions in the present application, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic flow chart diagram illustrating an embodiment of a method for estimating a user age provided herein;
FIG. 2 is a schematic flowchart of another embodiment of a method for estimating a user age provided in the present application;
FIG. 3 is a schematic structural diagram of an embodiment of a user age estimation device provided in the present application;
fig. 4 is a schematic structural diagram of another embodiment of the user age estimation device provided in the present application.
Detailed Description
In practice, users of different ages use intelligent devices such as computers and mobile phones, and preferences of application software are different, when a user makes a transaction on the application software or the user registers after downloading the application software, the application software requires the user to input registration information, and in the prior art, the user's age is generally known through the registration information.
Based on this, the present application provides a user age estimation method for estimating the age of a user based on installed application software.
The user age estimation method provided by the present application may be applied to a terminal, a system, or a server, for example, the terminal may be a smartphone, a computer, a tablet computer, a smart television, a smart watch, a portable computer terminal, or a fixed terminal such as a desktop computer. For convenience of explanation, the terminal is taken as an execution subject for illustration in the present application.
Referring to fig. 1, fig. 1 is a schematic flow chart of an embodiment of a user age estimation method provided in the present application, the user age estimation method includes:
101. reading application data of installed application software of a user;
the method provided by the application is used for estimating the ages of users, and the users with different ages are influenced by the levels and the surrounding environment, so that the installation and the use of the application software are different, and generally, the method can be represented by different types of the application software, different use durations of the application software, different use time points of the application software, different use frequencies of the application software and the like. The method provided by the application is based on the influence factors, and provides a user age estimation method, which is used for receiving and reading application data of application software installed by a user, wherein the application data of the application software comprises the application software installed by the user, use data of each application software and the like.
Furthermore, the application data may also include description information of the application software, where the description information may record release information of the application software, such as some information of a publisher, a use object, a suggested age group of the use object, and the application software itself, or others, and the description information may be information created when the publisher releases the application software, or evaluation description information of the application software by a user.
102. Generating a corresponding list text according to the application data;
in order to better sort and screen the application data and perform subsequent analysis and use, the application data is generated into a list text (txt), and the list text can record data such as the use time point, the use duration and the use frequency of application software.
In an alternative embodiment, if the application software contains the description information, the generation of the corresponding list text according to the application software data may be to extract a keyword in the description information and the occurrence number of the keyword, and record the data in the list text.
103. Screening the application software according to the use time point, the use duration and the use frequency of the application software recorded in the list text to obtain a screened list text;
screening the application software according to various types of data recorded in the list text to remove a part of invalid or data which may affect the accuracy of speculation, wherein the screening may have various strategies, for example, the application software corresponding to the application software which is not used at a time point exceeding a preset time point may be removed from the list text; the non-use beyond the preset time point means that the time of the last use exceeds a certain time period to the statistical time, for example, the non-use exceeds three months. Such long-term unused application software is likely to be misloaded and not have very high representative characteristics. Removing application data corresponding to the application software with the use duration not reaching the preset duration from the list text; for example, the application software with the use time of less than 5 minutes is eliminated, and the application software is likely to be the software pre-installed in the system or some non-representative tool software, such as a calendar, a compass and the like. And removing application data corresponding to the application software of which the use frequency does not reach the preset frequency from the list text. The use frequency can better reflect the characteristics of the user and eliminate some applications with lower use frequency.
104. Inputting the screened list texts serving as a speculative data set into a pre-selected trained random forest classifier, wherein the random forest classifier is obtained by training through a sample data set, and the sample data set comprises a plurality of list texts carrying application software of user ages;
the method comprises the steps of screening list texts, inputting the screened list texts into a pre-trained random forest classifier, and analyzing the list texts, wherein the random forest belongs to one of the classifiers, the random forest is an algorithm integrating a plurality of trees through an Ensemble Learning idea, a basic unit of the random forest is a decision tree, and the essence of the random forest belongs to a large branch of machine Learning, namely an Ensemble Learning (Ensemble Learning) method. A decision tree is a tree-like structure in which each internal node represents a test on an attribute, each branch represents a test output, and each leaf node represents a category. Common decision tree algorithms are C4.5, ID3, and CART. Ensemble learning solves a single prediction problem by building several model combinations. Its working principle is to generate multiple classifiers/models, each of which learns and makes predictions independently. These predictions are eventually combined into a single prediction and therefore are superior to making predictions for any single classification. Random forests are a subclass of ensemble learning that relies on voting choices of decision trees to determine the final classification result.
105. And outputting a presumption result of the user age of the user through the random forest classifier.
Analyzing the application data of the application software in the list text by using the random forest classifier, for example, using the type of the application software, the corresponding frequency of use, the duration of use, the time point of use, and other inferred data as input parameters of the random forest classifier, and finally outputting an inferred result of the age of the user, specifically, please refer to the embodiment corresponding to fig. 2 for the embodiment of the training method of the random forest classifier.
Referring to fig. 2, the random forest classifier in the present application is obtained by training as follows:
201. acquiring a first sample data set, inputting the first sample data set into an initialized random forest classifier, and training the initialized random forest classifier, wherein the first sample data set comprises a list text of application software carrying the age of a user;
in this embodiment, a semi-supervised scheme is adopted to train the random forest classifier, and first initial sample data is obtained and output to an initialized random forest classifier, where the initialized random forest classifier is an untrained classifier with basic parameters, and the first sample data carries a list text of a plurality of known user ages, where the list text includes application data of application software and ages of corresponding users. I.e. training the initialized random forest using the known first sample data.
202. Inputting the second sample data set into the trained random forest classifier, conjecturing the age of the user, and determining a third sample data set with a confidence coefficient reaching a preset confidence coefficient threshold;
and inputting the second sample data set into a random forest classifier trained by using the first sample data, inferring the age of the user, and then obtaining third sample data with higher confidence coefficient, wherein the higher confidence coefficient means that the third sample data reaches a preset confidence coefficient threshold value.
203. And setting the confidence coefficient of the third sample data to be the highest, and inputting the third sample data set and the second sample data set into the trained random forest classifier again for training until the random forest classifier is converged to obtain the random forest classifier.
And setting the confidence coefficient of the third sample data with higher confidence coefficient as the highest confidence coefficient, inputting the third sample data and the second sample data as training data into the trained random forest classifier again for training, and repeating continuously until the random forest classifier converges, or the data with no confidence coefficient or lower confidence coefficient is completely consumed.
The random forest classifier is trained through the semi-supervised method, so that the accuracy of the random forest classifier in conjecture of the user age can be effectively improved.
The above-described embodiments have explained the user age estimation method provided in the present application, and the user age estimation apparatus and the storage medium provided in the present application will be explained below.
Referring to fig. 3, the user age estimation apparatus provided in the present application includes:
a reading unit 301 configured to read application data of installed application software of a user;
a generating unit 302, configured to generate a corresponding list text according to the application data;
the screening unit 303 is configured to screen the application software according to the usage time point, the usage duration, and the usage frequency of the application software recorded in the list text, so as to obtain a screened list text;
an input unit 304, configured to input the screened list text as a speculative data set into a pre-selected trained random forest classifier, where the random forest classifier is obtained by training a sample data set, and the sample data set includes a plurality of list texts carrying application software of the user age;
an output unit 305 for outputting a result of the presumption of the user's age by the random forest classifier.
Optionally, the screening unit 303 is specifically configured to:
removing application data corresponding to unused application software exceeding a preset time point from the list text;
removing application data corresponding to the application software with the use duration not reaching the preset duration from the list text;
and removing application data corresponding to the application software of which the use frequency does not reach the preset frequency from the list text.
Optionally, the generating unit 302 is specifically configured to:
and extracting the keywords in the description information and recording the keywords in the list text.
Optionally, the apparatus further comprises:
a training unit 306 for:
acquiring a first sample data set, inputting the first sample data set into an initialized random forest classifier, and training the initialized random forest classifier, wherein the first sample data set comprises a list text of application software carrying the age of a user;
inputting the second sample data set into the trained random forest classifier, conjecturing the age of the user, and determining a third sample data set with a confidence coefficient reaching a preset confidence coefficient threshold;
and setting the confidence coefficient of the third sample data to be the highest, and inputting the third sample data set and the second sample data set into the trained random forest classifier again for training until the random forest classifier is converged to obtain the random forest classifier.
Referring to fig. 4, the present application further provides a user age estimation device, including:
a processor 401, a memory 402, an input-output unit 403, a bus 404;
the processor 401 is connected to the memory 402, the input/output unit 403, and the bus 404;
the memory 402 holds a program that the processor 401 calls to perform any of the user age estimation methods described above.
The present application also relates to a computer-readable storage medium having a program stored thereon, wherein the program, when executed on a computer, causes the computer to perform any of the above-described user age estimation methods.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and the like.
Claims (10)
1. A method for estimating age of a user, the method comprising:
reading application data of installed application software of a user;
generating a corresponding list text according to the application data;
screening the application software according to the use time point, the use duration and the use frequency of the application software recorded in the list text to obtain a screened list text;
inputting the screened list text serving as a speculative data set into a pre-selected trained random forest classifier, wherein the random forest classifier is obtained by training a sample data set, and the sample data set comprises a plurality of list texts carrying application software of user ages;
and outputting a result of conjecture of the user age of the user through the random forest classifier.
2. The method of claim 1, wherein the filtering the application software according to the usage time point, the usage duration, and the usage frequency of the application software recorded in the list text comprises:
removing application data corresponding to unused application software exceeding a preset time point from the list text;
removing application data corresponding to the application software with the use duration not reaching the preset duration from the list text;
and removing application data corresponding to the application software of which the use frequency does not reach the preset frequency from the list text.
3. The method for estimating user age according to claim 1, wherein the application data includes description information of the application software, and the generating of the corresponding list text based on the application data includes:
and extracting the keywords in the description information and recording the keywords in a list text.
4. The user age estimation method according to claim 3, wherein the description information is information obtained based on release information of the application software in the application market.
5. The method of claim 1, wherein the random forest classifier is trained by:
acquiring a first sample data set, inputting the first sample data set into an initialized random forest classifier, and training the initialized random forest classifier, wherein the first sample data set comprises a list text of application software carrying the age of a user;
inputting the second sample data set into a trained random forest classifier, conjecturing the age of the user, and determining a third sample data set with a confidence coefficient reaching a preset confidence coefficient threshold;
setting the confidence coefficient of the third sample data to be the highest, and inputting the third sample data set and the second sample data set into the trained random forest classifier again for training until the random forest classifier is converged to obtain the random forest classifier.
6. A user age estimation apparatus, characterized in that the apparatus comprises:
a reading unit for reading application data of installed application software of a user;
the generating unit is used for generating a corresponding list text according to the application data;
the screening unit is used for screening the application software according to the use time point, the use duration and the use frequency of the application software recorded in the list text to obtain a screened list text;
the input unit is used for inputting the screened list texts serving as a speculative data set into a pre-selected trained random forest classifier, wherein the random forest classifier is obtained by training a sample data set, and the sample data set comprises a plurality of list texts carrying application software of the user age;
and the output unit is used for outputting the estimation result of the user age of the user through the random forest classifier.
7. The user age estimation device according to claim 6, wherein the filtering unit is configured to:
removing application data corresponding to unused application software exceeding a preset time point from the list text;
removing application data corresponding to the application software with the use duration not reaching the preset duration from the list text;
and removing application data corresponding to the application software of which the use frequency does not reach the preset frequency from the list text.
8. The user age estimation device according to claim 6, wherein the generation unit is configured to:
and extracting the keywords in the description information and recording the keywords in a list text.
9. A user age estimation apparatus, characterized in that the apparatus comprises:
the device comprises a processor, a memory, an input and output unit and a bus;
the processor is connected with the memory, the input and output unit and the bus;
the memory holds a program that the processor calls to perform the method of any of claims 1 to 5.
10. A computer-readable storage medium having a program stored thereon, the program, when executed on a computer, performing the method of any one of claims 1 to 5.
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