User portrait generation method and apparatus, and electronic device

Abstract

The application provides a user portrait generation method and device and electronic equipment. The method comprises: obtaining user data of a target user, wherein the user data comprises structured user data and unstructured user data; performing data recognition processing on the user data based on a concept network and a preset topic model to determine label information of the target user and probability information corresponding to the label information, wherein the probability information is used to represent the credibility of the corresponding label information; and generating a user portrait of the target user according to the label information and the probability information corresponding to the label information. In this process, the concept network knowledge graph is introduced, the concept network is introduced into the data feature recognition process, and the user portrait construction accuracy is improved. The unstructured user data is introduced into the construction of the user portrait, the user data is enriched, and the user portrait construction accuracy is improved.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a user profile generation method, apparatus and electronic device. Background Technology

[0002] The financial industry is a data-intensive industry with high requirements for the practicality of information systems. Utilizing big data for analysis and research is an irreversible development trend in the financial industry. Therefore, improving the accuracy of data identification is a very important research topic.

[0003] In existing technologies, topic models are used to extract topic features, and then user profiles are constructed based on these topic features.

[0004] However, in existing technologies, the use of topic models alone to determine topic features is insufficient, leading to inaccurate feature extraction and inaccurate user profiles. Summary of the Invention

[0005] This application provides a user profile generation method, apparatus, and electronic device to solve the problem of inaccurate user profiles.

[0006] Firstly, this application provides a user profile generation method, the method comprising:

[0007] Obtain user data of the target user, wherein the user data includes structured user data and unstructured user data;

[0008] Based on concept networks and preset topic models, the user data is processed for data identification to determine the tag information of the target user and the probability information corresponding to the tag information, wherein the probability information is used to characterize the credibility of the corresponding tag information;

[0009] A user profile of the target user is generated based on the tag information and the probability information corresponding to the tag information.

[0010] In one optional implementation, the user data is processed for data identification based on a concept network and a preset topic model to determine the tag information of the target user and the probability information corresponding to the tag information, including:

[0011] Based on the conceptual network, the user data is processed by data splicing to generate input data, wherein the input data is the input data of the preset topic model;

[0012] Based on the preset topic model, the input data is identified, and the tag information of the target user and the probability information corresponding to the tag information are determined.

[0013] In one optional implementation, based on the conceptual network, the user data is subjected to data concatenation processing to generate input data, including:

[0014] Based on the conceptual network, determine the weight information corresponding to the user data;

[0015] The user data is concatenated based on the weight information to generate input data.

[0016] In one optional implementation, based on the preset topic model, the input data is identified, and the tag information of the target user and the probability information corresponding to the tag information are determined, including:

[0017] Based on the preset topic model, extract the data feature information of the input data;

[0018] Based on the data feature information, the target user's tag information and the probability information corresponding to the tag information are generated.

[0019] In one optional implementation, before performing data identification processing on the user data based on a concept network and a preset topic model, the method further includes:

[0020] The user data is preprocessed, which includes one or more of the following: word segmentation, stop word removal, and data filtering.

[0021] In one optional implementation, the method further includes:

[0022] Based on the user profile, recommendation information for the target user is generated, and information is recommended to the target user based on the recommendation information.

[0023] Secondly, this application provides a user profile generation device, the device comprising:

[0024] An acquisition unit is used to acquire user data of a target user, wherein the user data includes structured user data and unstructured user data;

[0025] The first processing unit is used to perform data recognition processing on the user data based on the concept network and the preset topic model, and to determine the tag information of the target user and the probability information corresponding to the tag information, wherein the probability information is used to characterize the credibility of the corresponding tag information;

[0026] The generation unit is used to generate a user profile of the target user based on the tag information and the probability information corresponding to the tag information.

[0027] Thirdly, this application provides an electronic device, including a memory and a processor;

[0028] The memory is used to store computer programs;

[0029] The processor is configured to read the computer program stored in the memory and execute the user profile generation method as described in the first aspect according to the computer program in the memory.

[0030] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the user profile generation method as described in the first aspect.

[0031] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the user profile generation method as described in the first aspect.

[0032] The user profile generation method, apparatus, and electronic device provided in this application, through the following steps: acquiring user data of the target user, wherein the user data includes structured user data and unstructured user data; performing data recognition processing on the user data based on concept networks and preset topic models to determine the target user's tag information and the probability information corresponding to the tag information, wherein the probability information is used to characterize the credibility of the corresponding tag information; generating a user profile of the target user based on the tag information and the probability information corresponding to the tag information. In this process, a concept network knowledge graph is introduced, and by introducing the concept network into the data feature recognition process, the accuracy of user profile construction is improved; and introducing unstructured user data into the construction of the user profile enriches the user data and improves the accuracy of user profile construction. Attached Figure Description

[0033] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0034] Figure 1 A flowchart illustrating a user profile generation method provided in an embodiment of this application;

[0035] Figure 2 A flowchart illustrating another user profile generation method provided in this application embodiment;

[0036] Figure 3 This is a schematic diagram of the structure of a user profile generation device provided in an embodiment of this application;

[0037] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application;

[0038] Figure 5 This is a block diagram of an electronic device provided in an embodiment of this application.

[0039] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0040] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0041] The rapid development of the internet has resulted in the pervasive tracking of user behavior data. This data holds immense commercial value and can be fully leveraged, within compliant limits, to better understand users and provide them with superior product or service experiences. The financial industry, being data-intensive, demands high-performance information systems. The application of financial big data can improve resource allocation efficiency, strengthen risk management capabilities, and effectively promote innovative development in financial services. By effectively utilizing big data through user analysis, risk and credit assessment, and transaction fraud detection, personalized information recommendations and enhanced risk management can be achieved. Therefore, using big data for analysis and research is an irreversible trend in the financial industry. Improving the accuracy and usability of data identification is thus a crucial research area.

[0042] In one example, a user persona represents a virtual user and is a three-dimensional user model composed of a series of data. Simply put, user persona is a user analysis method that uses various behavioral data to create tags for users, concretizing each user's image and striving to objectively and accurately describe user characteristics. User persona has proven highly effective and adaptable, and as an effective tool for target user analysis, it has been rapidly and widely applied in disciplines such as computer science and library and information science. The process of generating user persona tags can be divided into several parts: raw data acquisition and preprocessing, tag selection and indicator system construction, and user persona tag generation model construction.

[0043] Current research on user profiling in the banking industry is limited. Existing methods for generating user profile tags based on topic models generally involve preprocessing the data to be processed, directly using topic models to extract topic features, and finally processing these features to construct the user profile tags. A topic model is a statistical model that uses unsupervised learning to cluster the implicit semantic structure of a collection of documents. Topic models are mainly used in semantic analysis and text mining problems in natural language processing, such as collecting, classifying, and reducing the dimensionality of text by topic. Latent Dirichlet Allocation (LDA) is a common topic model, and the Biterm Topic Model (BTM) is an improved version of LDA for short text data sources. Existing technologies have the following drawbacks:

[0044] (1) Insufficient accuracy due to limited user behavior data: Existing user profiling technologies are often based on statistical theories, and the richness of the data source greatly affects the final model's effectiveness. However, most existing user profiling technologies rely solely on basic user attributes, transaction behavior attributes, or social behavior attributes, resulting in insufficient accuracy of the final results.

[0045] (2) Insufficient introduction of external knowledge: User profile building technology based on topic model often extracts user feature vectors by topic model such as Latent Dirichlet Allocation (LDA). The introduction of external knowledge is slightly insufficient, resulting in the general quality of the extracted features and thus the poor effect of user profile generation.

[0046] (3) Insufficient unstructured data: Most current user profiling technologies are designed for research on structured data and use less unstructured text data, which leads to inaccurate user analysis.

[0047] The user profile generation method provided in this application aims to solve the above-mentioned technical problems of the prior art.

[0048] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0049] Figure 1 This is a flowchart illustrating a user profile generation method provided in an embodiment of this application, as shown below. Figure 1As shown, the method includes:

[0050] 101. Obtain user data of the target user, including structured user data and unstructured user data.

[0051] For example, user data of target users is acquired, including structured and unstructured user data. Structured data, also known as quantitative data, includes information such as account information, dates, financial information, phone numbers, address information, and product names. Unstructured data is essentially all data other than structured data. It does not conform to any predefined model, is stored in a non-relational database, and is queried using a non-relational database. Unstructured user data may be text-based or non-text-based, may be human-generated or machine-generated, and is data with variable fields, such as user reviews.

[0052] In one example, depending on how users participate, the data acquisition process can be divided into two types: explicit acquisition and implicit acquisition. Explicit acquisition of user data refers to obtaining basic information, behavioral preferences, and other relevant data manually entered by the user. Implicit acquisition of data is transparent to the user and mainly includes recording user information during user interactions and analyzing user behavior habits to obtain user characteristics. The richer the data acquired, the more comprehensive the generated user profile will be.

[0053] 102. Based on concept networks and pre-defined topic models, perform data identification processing on user data to determine the tag information of target users and the probability information corresponding to the tag information. The probability information is used to characterize the credibility of the corresponding tag information.

[0054] For example, based on a concept network and a pre-defined topic model, such as a Biterm Topic Model (BTM), data feature identification and extraction processing is performed on the acquired structured user data and unstructured user data to determine the target user's tag information and the probability information corresponding to the tag information, wherein the probability information is used to characterize the credibility of the corresponding tag information.

[0055] In one example, a concept net is a knowledge graph in which natural language words and phrases are interconnected by edges with labels and weights (representing the credibility of the edges). The labels indicate the type of the edge, and the weights indicate the credibility of the edge.

[0056] 103. Generate a user profile of the target user based on the tag information and the probability information corresponding to the tag information.

[0057] For example, a user profile of the target user can be generated based on the tag information and the probability information corresponding to the tag information. For instance, a word cloud method can be used to generate the user profile of the target user.

[0058] In summary, the user profile generation method provided in this embodiment involves the following steps: acquiring user data of the target user, wherein the user data includes structured user data and unstructured user data; performing data recognition processing on the user data based on a concept network and a preset topic model to determine the target user's tag information and the probability information corresponding to the tag information, wherein the probability information is used to characterize the credibility of the corresponding tag information; generating a user profile of the target user based on the tag information and the probability information corresponding to the tag information. In this process, a concept network knowledge graph is introduced, which improves the accuracy of user profile construction by introducing the concept network into the data feature recognition process; and introducing unstructured user data into the construction of the user profile enriches the user data and improves the accuracy of user profile construction.

[0059] Figure 2 A flowchart illustrating another user profile generation method provided in this application embodiment is shown below. Figure 2 As shown, the method includes:

[0060] 201. Obtain user data of the target user, including structured user data and unstructured user data.

[0061] For example, this step is the same as step 101, and will not be repeated here.

[0062] 202. Perform data preprocessing on user data, including one or more of the following: word segmentation, stop word removal, and data filtering.

[0063] For example, user data preprocessing can be performed to obtain standardized data suitable for user profile generation. The main preprocessing methods include word segmentation, stop word removal, data filtering, and data normalization. Word segmentation is an essential step in processing unstructured user data; data filtering ensures data reliability; and data normalization maintains the consistency of the input model data.

[0064] 203. Based on the concept network, the user data is processed by data splicing to generate input data, where the input data is the input data of the preset topic model.

[0065] In one example, step 203 includes the following steps:

[0066] Based on the conceptual network, determine the weight information corresponding to the user data;

[0067] The user data is concatenated based on the weight information to generate the input data.

[0068] For example, based on a concept network, user data is processed by data splicing to generate input data for a topic model. For instance, based on a concept network, user data is labeled with credibility to determine the weight information corresponding to the user data; and user data is spliced ​​according to the weight information to generate input data for a preset topic model.

[0069] 204. Based on the preset topic model, identify the input data and determine the target user's tag information and the probability information corresponding to the tag information.

[0070] In one example, step 204 includes the following steps:

[0071] Based on the pre-defined topic model, extract the data feature information of the input data;

[0072] Based on data feature information, the target user's tag information and the probability information corresponding to the tag information are generated.

[0073] For example, based on a pre-defined topic model, such as a two-topic model, data feature information is extracted from the input data. Based on this data feature information, tag information for the target user and corresponding probability information are generated. For instance, data feature extraction processing is performed on the model input data using a two-topic model, analyzing the user behavior contained in the data. This user behavior is then combined with specific business operations to extract more extensive data feature information. For example, user purchase frequency data is a statistical analysis of a user's actual purchase behavior over a period of time. Based on this, by constructing a correlation between this data and business issues, more extensive data feature information is extracted to obtain tag information and its credibility probability information suitable for user profiling. For example, by combining the user's actual purchase frequency, the type of product purchased, and the purchase amount, the user's purchase tendency type can be identified.

[0074] 205. Generate a user profile of the target user based on the tag information and the probability information corresponding to the tag information.

[0075] For example, a user profile of the target user can be generated based on the tag information and the probability information corresponding to the tag information. For instance, a word cloud method can be used to generate the user profile of the target user.

[0076] In one example, the target user can be multiple users, and the corresponding user data can also be user data of multiple users. After generating the label information of the target user and the probability information corresponding to the label information, the users can be clustered using a clustering algorithm, such as the k-means clustering algorithm, to generate a user profile of users with similar labels.

[0077] 206. Based on the user profile, generate recommendation information for the target user, and recommend information to the target user based on the recommendation information.

[0078] For example, based on the generated user profile of the target user, corresponding recommendation information is generated, and personalized information recommendations and personalized services are provided for the target user.

[0079] In summary, the user profile generation method provided in this embodiment ensures the reliability and availability of user data by preprocessing the user data, thereby improving the accuracy of the constructed user profile; it determines the weight information corresponding to the user data through a concept network; it performs data splicing processing on the user data according to the weight information to generate input data, introducing external knowledge and improving the accuracy of the constructed user profile; and it expands the data feature dimensions based on a topic model, making the generated user profile richer.

[0080] Figure 3 This is a schematic diagram of the structure of a user profile generation device provided in an embodiment of this application, as shown below. Figure 4 As shown, the device includes:

[0081] The acquisition unit 31 is used to acquire user data of the target user, wherein the user data includes structured user data and unstructured user data.

[0082] The first processing unit 32 is used to perform data recognition processing on user data based on concept networks and preset topic models, and to determine the tag information of the target user and the probability information corresponding to the tag information, wherein the probability information is used to characterize the credibility of the corresponding tag information.

[0083] The generation unit 33 is used to generate a user profile of the target user based on the tag information and the probability information corresponding to the tag information.

[0084] In one example, the first processing unit 32 includes;

[0085] The first processing subunit is used to perform data splicing processing on user data based on the concept network to generate input data, wherein the input data is the input data of the preset topic model.

[0086] The second processing subunit is used to identify input data and determine the target user's tag information and the probability information corresponding to the tag information based on the preset topic model.

[0087] In one example, the first processing subunit includes:

[0088] The determination module is used to determine the weight information corresponding to user data based on the conceptual network.

[0089] The first processing module is used to perform data splicing on user data according to weight information to generate input data.

[0090] In one example, the second processing subunit includes:

[0091] The second processing module is used to extract data feature information from the input data based on a preset topic model.

[0092] The third processing module is used to convert and generate the target user's tag information and the probability information corresponding to the tag information based on the data feature information.

[0093] In one example, the apparatus further includes, prior to the first processing unit 32:

[0094] The preprocessing unit is used to preprocess user data, including one or more of word segmentation, stop word removal, and data filtering.

[0095] In one example, the device also includes:

[0096] The second processing unit is used to generate recommendation information for target users based on user profiles, and to recommend information to target users based on the recommendation information.

[0097] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, such as... Figure 4 As shown, the electronic device includes:

[0098] Memory 41 is used to store computer programs.

[0099] The processor 42 is used to read the computer program stored in the memory and execute the user profile generation method of the above embodiment according to the computer program in the memory.

[0100] Figure 5 This is a block diagram of an electronic device provided in an embodiment of this application. The device may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

[0101] The device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814, and a communication component 816.

[0102] Processing component 802 typically controls the overall operation of device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 802 may include one or more processors 820 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0103] Memory 804 is configured to store various types of data to support the operation of device 800. Examples of such data include instructions for any application or method operating on device 800, contact data, phonebook data, messages, pictures, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0104] Power supply component 806 provides power to various components of device 800. Power supply component 806 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power to device 800.

[0105] Multimedia component 808 includes a screen that provides an output interface between device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of touch or swipe actions but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When device 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0106] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0107] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0108] Sensor assembly 814 includes one or more sensors for providing state assessments of various aspects of device 800. For example, sensor assembly 814 may detect the on / off state of device 800, the relative positioning of components such as the display and keypad of device 800, changes in the position of device 800 or a component of device 800, the presence or absence of user contact with device 800, the orientation or acceleration / deceleration of device 800, and temperature changes of device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0109] Communication component 816 is configured to facilitate wired or wireless communication between device 800 and other devices. Device 800 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0110] In an exemplary embodiment, the apparatus 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0111] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, which can be executed by a processor 820 of the device 800 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0112] This application also provides a non-transitory computer-readable storage medium, which, when the instructions in the storage medium are executed by the processor of an electronic device, enables the electronic device to perform the above-described method.

[0113] This application also provides a computer program product, comprising: a computer program stored in a readable storage medium, wherein at least one processor of an electronic device can read the computer program from the readable storage medium, and the at least one processor executes the computer program to cause the electronic device to perform the scheme provided in any of the above embodiments.

[0114] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0115] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for generating user profiles, characterized in that, The method includes: Obtain user data of the target user, wherein the user data includes structured user data and unstructured user data; Based on the conceptual network, the weight information corresponding to the user data is determined; The user data is concatenated according to the weight information to generate input data; wherein, the input data is the input data of a preset topic model; Based on the preset topic model, extract the data feature information of the input data; Based on the data feature information, the target user's tag information and the probability information corresponding to the tag information are generated, and the probability information is used to characterize the credibility of the corresponding tag information; A user profile of the target user is generated based on the tag information and the probability information corresponding to the tag information.

2. The method according to claim 1, characterized in that, Before performing data identification processing on the user data based on conceptual networks and preset topic models, the method further includes: The user data is preprocessed, which includes one or more of the following: word segmentation, stop word removal, and data filtering.

3. The method according to claim 1 or 2, characterized in that, The method further includes: Based on the user profile, recommendation information for the target user is generated, and information is recommended to the target user based on the recommendation information.

4. A user profile generation device, characterized in that, The device includes: An acquisition unit is used to acquire user data of a target user, wherein the user data includes structured user data and unstructured user data; The first processing unit is used to perform data recognition processing on the user data based on the concept network and the preset topic model, and to determine the tag information of the target user and the probability information corresponding to the tag information, wherein the probability information is used to characterize the credibility of the corresponding tag information; The generation unit is used to generate a user profile of the target user based on the tag information and the probability information corresponding to the tag information; The first processing unit is specifically configured to: determine the weight information corresponding to the user data based on the concept network; perform data concatenation processing on the user data according to the weight information to generate input data; wherein the input data is the input data of the preset topic model; extract the data feature information of the input data according to the preset topic model; and convert and generate the tag information of the target user and the probability information corresponding to the tag information according to the data feature information.

5. An electronic device, characterized in that, Including memory and processor; The memory is used to store computer programs; The processor is configured to read the computer program stored in the memory and execute the user profile generation method according to any one of claims 1-3 based on the computer program in the memory.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by the processor, implement the user profile generation method as described in any one of claims 1-3.

7. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the user profile generation method according to any one of claims 1-3.

Images