Label-based user group management method, system, equipment and medium

Abstract

The invention discloses a label-based user group management method, system and device and a medium, belongs to the technical field of information, and aims to solve the technical problems of low efficiency, limited accuracy and insufficient flexibility in the existing user management technology. The multi-dimensional label library comprises a plurality of labels related to user behaviors, interests and attributes; data collection and preprocessing: collecting behavior data, interest data and basic attribute data of the user; user data tagging: according to the collected data and a preset rule, automatically generating a corresponding tag for the user and storing the tag in a user tag library; group label definition: a group management interface is provided, a group of labels are defined for the groups, and the corresponding labels are used for reflecting core characteristics and member generality of the corresponding groups; label matching and user grouping: distributing the users to one or more groups according to the labels in the user label library; and maintaining and optimizing the group.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a tag-based user group management method, system, device and medium. Background Art

[0002] In today's digital age, with the popularity of the Internet and mobile devices, the scale of user data has shown exponential growth. Enterprises and service providers will face hundreds of millions of user groups and a large amount of user behavior data, which contains valuable information such as user preferences, habits, and needs. Traditional user management methods mainly rely on manual analysis and simple data grouping, such as dividing users based on their basic attributes (age, gender, and geographic location). However, this method has limitations such as low efficiency, limited accuracy, insufficient flexibility, and limited personalized services. Summary of the invention

[0003] The technical task of the present invention is to provide a tag-based user group management method, system, device and medium to solve the problems of low efficiency, limited accuracy and insufficient flexibility in existing user management technologies.

[0004] The technical task of the present invention is achieved in the following way: a tag-based user group management method, the method is as follows:

[0005] Create a tag library: Create a multidimensional tag library that contains multiple tags related to user behavior, interests, and attributes;

[0006] Data collection and preprocessing: Collect user behavior data, interest data, and basic attribute data;

[0007] User data labeling: Based on the collected data and preset rules, corresponding labels are automatically generated for users and stored in the user label library;

[0008] Group label definition: Provides a group management interface to define a set of labels for the group. The corresponding labels are used to reflect the core characteristics of the corresponding group and the common characteristics of its members.

[0009] Tag matching and user grouping: assign users to one or more groups based on the tags in the user tag library;

[0010] Group maintenance and optimization: Regularly evaluate the effectiveness of group allocation, adjust group settings based on user feedback and behavior data, and continuously optimize the SVM model to optimize user tags and group configuration.

[0011] As a preference, labels are divided into basic attribute labels, statistical labels, and manual labels;

[0012] Among them, basic attribute tags refer to data directly obtained from the business platform data source. Basic attribute tags include regional attribute tags, group attribute tags, gender tags, and time attribute tags (user registration time);

[0013] Statistical tags refer to active day tags, browsing preference tags, and credit score tags generated when obtaining and analyzing user operation behaviors and interaction data;

[0014] Manual labeling refers to the function of manually creating and labeling labels; manual labeling includes resource content labeling and user telephone consultation frequency labeling;

[0015] Among them, creating a multi-dimensional tag library also includes an optimization process for dynamically adjusting the content and structure of the tag library based on user feedback and usage.

[0016] As a preference, data collection and preprocessing are specifically as follows:

[0017] Explicit data input: This includes name, address, phone number, and email information provided by users when registering an account, filling out forms, questionnaires, or making a purchase;

[0018] Implicit data collection: collect user behavior and interaction data through system tracking. User behavior and interaction data include browsing history data, click-through rate data, time spent on the website, and search history data;

[0019] Cookies and usage logs: Websites and applications use cookies to record user preferences and history; server log files are used to record user IP addresses, browser types, and access time information;

[0020] API integration: Use application programming interfaces (APIs) to obtain data from other services. For example, the APIs provided by social media platforms can obtain information such as users' public posts.

[0021] Third-party data: Purchase or receive data from partners or other third parties. Third-party data includes user credit data and record data of participation in any business.

[0022] As a preferred method, user data labeling is as follows:

[0023] Rule-based labeling: preset labeling rules and conditions, through the data processing tool (kettle), automatically labeling users who meet the conditions; for example: labeling "male" or "female" according to the gender of the user; labeling the user according to the region attribute;

[0024] Behavior-based labeling: By building a SVM model and selecting the radial basis function (RBF) kernel, the collected implicit data is trained and analyzed, and finally the user is labeled with a behavior label.

[0025] Preferably, group tags can be dynamically updated to adapt to changes in system and user behavior.

[0026] Preferably, tag matching and user grouping are as follows:

[0027] Automatically assign users to corresponding groups based on the matching degree between user tags and group tags;

[0028] Provides a manual adjustment function, allowing administrators to intervene in user grouping based on special circumstances.

[0029] A tag-based user group management system, the system comprising:

[0030] A tag library creation module is used to create a multi-dimensional tag library, which contains multiple tags related to user behavior, interests and attributes;

[0031] Data collection and preprocessing module, used to collect user behavior data, interest data and basic attribute data;

[0032] The tag generator is used to automatically generate corresponding tags for users based on the collected data and preset rules and store them in the user tag library;

[0033] The group label definition module is used to provide a group management interface and define a set of labels for the group. The corresponding labels are used to reflect the core characteristics of the corresponding group and the common characteristics of its members.

[0034] A group assignment module is used to assign users to one or more groups according to tags in the user tag library;

[0035] The group maintenance and optimization module is used to regularly evaluate the effectiveness of group allocation, adjust group settings based on user feedback and behavior data, and continuously optimize the SVM model to optimize user labels and group configurations.

[0036] Preferably, the system further comprises a feedback loop mechanism and a visualization tool;

[0037] Among them, the feedback loop mechanism is used to collect user response data to information push, and optimize the label generation group allocation strategy based on the corresponding data;

[0038] Visualization tools are used to display the distribution of user tags, group characteristics, and the effects of group management, helping administrators make decisions;

[0039] The data collection and preprocessing module also has data preprocessing functions, which are used to clean, standardize and normalize the collected data to improve data quality;

[0040] The tag generator uses the support vector machine (SVM) algorithm in the machine learning algorithm to identify and predict the user's hidden tags;

[0041] When assigning users to groups, the group assignment module balances the size of the groups and the diversity of users within the groups, ensuring that the user labels within each group are representative and evenly distributed.

[0042] An electronic device comprising: a memory and at least one processor;

[0043] Wherein, the memory stores a computer program;

[0044] The at least one processor executes the computer program stored in the memory, so that the at least one processor performs the above-mentioned tag-based user group management method.

[0045] A computer-readable storage medium stores a computer program, and the computer program can be executed by a processor to implement the above-mentioned tag-based user group management method.

[0046] The tag-based user group management method, system, device and medium of the present invention have the following advantages:

[0047] (1) The present invention solves the problems of low efficiency, limited accuracy and insufficient flexibility in existing user management technologies. By introducing innovative mechanisms such as automatic tag allocation, dynamic group update and personalized service provision, it achieves efficient processing and refined management of large-scale user data, thereby improving users' personalized service experience;

[0048] (ii) The present invention achieves efficient, accurate and flexible user management through automated tag allocation, dynamic group updating and personalized service provision;

[0049] (III) The present invention uses advanced data labeling and user grouping technologies to achieve refined management and dynamic updating of a large-scale user base, which can be applied to a variety of platforms to improve user satisfaction and service quality;

[0050] (IV) The present invention improves the efficiency of user management, as follows:

[0051] ①Automatic user classification: Through the support vector machine (SVM) model, user behavior can be automatically classified and labeled, reducing manual intervention and improving processing speed and accuracy;

[0052] ② Accurate user grouping: Based on user behavior tags, users can be more accurately divided into different groups to achieve targeted management;

[0053] (V) The present invention optimizes the user experience, as follows:

[0054] ① Personalized recommendations: Based on the user's behavior tags, content that is more in line with their interests and needs can be pushed to improve user satisfaction and loyalty;

[0055] ② Enhance user stickiness: Through refined user behavior analysis, continuously optimize product functions and service quality, thereby increasing user retention rate;

[0056] (VI) The present invention improves the quality of business decision-making and user behavior insights, as follows:

[0057] ① The SVM model can reveal complex patterns of user behavior, provide enterprises with in-depth business insights, and support strategic decision-making;

[0058] ② Market competitive advantage: With a deep understanding of user behavior, companies can respond quickly in the market, seize opportunities and improve their competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The present invention is further described below in conjunction with the accompanying drawings.

[0060] Attached Figure 1 The flowchart of the tag-based user group management method. DETAILED DESCRIPTION

[0061] The tag-based user group management method, system, device and medium of the present invention are described in detail below with reference to the accompanying drawings and specific embodiments.

[0062] Embodiment 1:

[0063] As attached Figure 1 As shown, this embodiment provides a tag-based user group management method, the method is as follows:

[0064] S1. Create a tag library: Create a multidimensional tag library, which contains multiple tags related to user behavior, interests and attributes;

[0065] S2, data collection and preprocessing: collect user behavior data, interest data and basic attribute data;

[0066] S3, user data labeling: according to the collected data and preset rules, automatically generate corresponding labels for users and store them in the user label library;

[0067] S4. Group label definition: Provide a group management interface to define a set of labels for the group. The corresponding labels are used to reflect the core characteristics and commonalities of the corresponding group members;

[0068] S5. Tag matching and user grouping: assign users to one or more groups based on the tags in the user tag library;

[0069] S6. Group maintenance and optimization: Regularly evaluate the effectiveness of group allocation, adjust group settings based on user feedback and behavioral data, and continuously optimize the SVM model to optimize user labels and group configuration.

[0070] The labels in step S1 of this embodiment are divided into basic attribute labels, statistical labels and manual labels;

[0071] Among them, basic attribute tags refer to data directly obtained from the business platform data source. Basic attribute tags include regional attribute tags, group attribute tags, gender tags, and time attribute tags (user registration time);

[0072] Statistical tags refer to active day tags, browsing preference tags, and credit score tags generated when obtaining and analyzing user operation behaviors and interaction data;

[0073] Manual labeling refers to the functions of manually creating and labeling labels; manual labeling includes resource content labeling labels and user telephone consultation frequency labels.

[0074] In this embodiment, creating a multi-dimensional tag library in step S1 also includes an optimization process for dynamically adjusting the content and structure of the tag library according to user feedback and usage.

[0075] The data collection and preprocessing in step S2 of this embodiment are specifically as follows:

[0076] S201, explicit data input: including name, address, phone number and email information provided by users when registering an account, filling out forms, questionnaires or shopping;

[0077] S202, implicit data collection: collect user behavior and interaction data through system embedding, and user behavior and interaction data include browsing history data, click-through rate data, time spent on the website and search history data;

[0078] S203, Cookies and Usage Logs: Websites and applications use cookies to record user preferences and history; server log files are used to record user IP addresses, browser types, and access time information;

[0079] S204, APIs integration: Use application programming interfaces (APIs) to obtain data from other services. For example, the APIs provided by social media platforms can obtain information such as users' public posts;

[0080] S205, third-party data: purchase or receive data from partners or other third parties. Third-party data includes user credit data and record data of participation in any business.

[0081] The user data labeling in step S3 of this embodiment is specifically as follows:

[0082] S301, rule-based labeling: preset labeling rules and conditions, automatically labeling users who meet the conditions through data processing tools (kettle); for example: labeling "male" or "female" according to users of different genders; labeling regional labels according to users' regional attributes;

[0083] S302, behavior-based labeling: By building a SVM model and selecting a radial basis function (RBF) kernel, the collected implicit data is trained and analyzed, and finally the user is labeled with a behavior label.

[0084] The group label in step S4 of this embodiment can be dynamically updated to adapt to changes in the system and user behavior.

[0085] The tag matching and user grouping in step S5 of this embodiment are specifically as follows:

[0086] S501, automatically classifying users into corresponding groups according to the matching degree between user tags and group tags;

[0087] S502. Provide a manual adjustment function to allow administrators to intervene in user grouping according to special circumstances.

[0088] Embodiment 2:

[0089] This embodiment provides a tag-based user group management system, the system comprising:

[0090] A tag library creation module is used to create a multi-dimensional tag library, which contains multiple tags related to user behavior, interests and attributes;

[0091] Data collection and preprocessing module, used to collect user behavior data, interest data and basic attribute data;

[0092] The tag generator is used to automatically generate corresponding tags for users based on the collected data and preset rules and store them in the user tag library;

[0093] The group label definition module is used to provide a group management interface and define a set of labels for the group. The corresponding labels are used to reflect the core characteristics of the corresponding group and the common characteristics of its members.

[0094] A group assignment module is used to assign users to one or more groups according to tags in the user tag library;

[0095] The group maintenance and optimization module is used to regularly evaluate the effectiveness of group allocation, adjust group settings based on user feedback and behavior data, and continuously optimize the SVM model to optimize user labels and group configurations.

[0096] In this embodiment, the system also includes a feedback loop mechanism and a visualization tool;

[0097] Among them, the feedback loop mechanism is used to collect user response data to information push, and optimize the label generation group allocation strategy based on the corresponding data;

[0098] Visualization tools are used to display the distribution of user tags, group characteristics, and the effects of group management, helping administrators make decisions;

[0099] The data collection and preprocessing module also has data preprocessing functions, which are used to clean, standardize and normalize the collected data to improve data quality;

[0100] The tag generator uses the support vector machine (SVM) algorithm in the machine learning algorithm to identify and predict the user's hidden tags;

[0101] When assigning users to groups, the group assignment module balances the size of the groups and the diversity of users within the groups, ensuring that the user labels within each group are representative and evenly distributed.

[0102] Embodiment 3:

[0103] This embodiment also provides an electronic device, including: a memory and at least one processor;

[0104] Wherein, the memory stores computer-executable instructions;

[0105] The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor executes the tag-based user group management method in any embodiment of the present invention.

[0106] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor may be a microprocessor or any conventional processor, etc.

[0107] The memory can be used to store computer programs and / or modules. The processor realizes various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the terminal, etc. In addition, the memory can also include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SMC), a secure digital (SD) card, a flash memory card, at least one disk storage period, a flash memory device, or other volatile solid-state storage devices.

[0108] Embodiment 4:

[0109] This embodiment also provides a computer-readable storage medium, in which a plurality of instructions are stored, and the instructions are loaded by a processor, so that the processor executes the tag-based user group management method in any embodiment of the present invention. Specifically, a system or device equipped with a storage medium can be provided, on which a software program code that implements the functions of any of the above embodiments is stored, and a computer (or CPU or MPU) of the system or device reads and executes the program code stored in the storage medium.

[0110] In this case, the program code itself read from the storage medium can realize the function of any one of the above-mentioned embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.

[0111] The storage medium embodiments for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RYM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program code can be downloaded from a server computer via a communication network.

[0112] In addition, it should be clear that the functions of any of the above embodiments can be implemented not only by executing the program code read by the computer, but also by enabling an operating system operating on the computer to complete part or all of the actual operations based on instructions from the program code.

[0113] In addition, it can be understood that the program code read from the storage medium is written to a memory provided in an expansion board inserted into the computer or written to a memory provided in an expansion unit connected to the computer, and then based on the instructions of the program code, a CPU installed on the expansion board or the expansion unit is enabled to perform part or all of the actual operations, thereby realizing the functions of any of the above-mentioned embodiments.

[0114] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A tag-based user group management method, characterized in that: The method is as follows: Create a tag library: Create a multidimensional tag library that contains multiple tags related to user behavior, interests, and attributes; Data collection and preprocessing: Collect user behavior data, interest data, and basic attribute data; User data labeling: Based on the collected data and preset rules, corresponding labels are automatically generated for users and stored in the user label library; Group label definition: Provides a group management interface to define a set of labels for the group. The corresponding labels are used to reflect the core characteristics of the corresponding group and the common characteristics of its members. Tag matching and user grouping: assign users to one or more groups based on the tags in the user tag library; Group maintenance and optimization: Regularly evaluate the effectiveness of group allocation, adjust group settings based on user feedback and behavior data, and continuously optimize the SVM model to optimize user tags and group configuration.

2. The tag-based user group management method according to claim 1, characterized in that: Labels are divided into basic attribute labels, statistical labels and manual labels; Among them, basic attribute tags refer to data directly obtained from the business platform data source. Basic attribute tags include regional attribute tags, group attribute tags, gender tags, and time attribute tags; Statistical tags refer to active day tags, browsing preference tags, and credit score tags generated when obtaining and analyzing user operation behaviors and interaction data; Manual labeling refers to the function of manually creating and labeling labels; manual labeling includes resource content labeling and user telephone consultation frequency labeling; Among them, creating a multi-dimensional tag library also includes an optimization process for dynamically adjusting the content and structure of the tag library based on user feedback and usage.

3. The tag-based user group management method according to claim 1, characterized in that: The data collection and preprocessing are as follows: Explicit data input: This includes name, address, phone number, and email information provided by users when registering an account, filling out forms, questionnaires, or making a purchase; Implicit data collection: collect user behavior and interaction data through system tracking. User behavior and interaction data include browsing history data, click-through rate data, time spent on the website, and search history data; Cookies and usage logs: Websites and applications use cookies to record user preferences and history; server log files are used to record user IP addresses, browser types, and access time information; API integration: Use application programming interfaces (APIs) to get data from other services; Third-party data: Purchase or receive data from partners or other third parties. Third-party data includes user credit data and record data of participation in any business.

4. The tag-based user group management method according to claim 1, characterized in that: User data labeling is as follows: Rule-based labeling: Preset labeling rules and conditions, and automatically label users who meet the conditions through data processing tools; Behavior-based labeling: By building a SVM model and selecting a radial basis function kernel, the collected implicit data is trained and analyzed, and finally the user is labeled with a behavior label.

5. The tag-based user group management method according to claim 1, characterized in that: Group labels can be updated dynamically to adapt to changes in system and user behavior.

6. The tag-based user group management method according to any one of claims 1 to 5, characterized in that: The details of tag matching and user grouping are as follows: Automatically assign users to corresponding groups based on the matching degree between user tags and group tags; Provides a manual adjustment function, allowing administrators to intervene in user grouping based on special circumstances.

7. A tag-based user group management system, characterized in that: The system includes: A tag library creation module is used to create a multi-dimensional tag library, which contains multiple tags related to user behavior, interests and attributes; Data collection and preprocessing module, used to collect user behavior data, interest data and basic attribute data; The tag generator is used to automatically generate corresponding tags for users based on the collected data and preset rules and store them in the user tag library; The group label definition module is used to provide a group management interface and define a set of labels for the group. The corresponding labels are used to reflect the core characteristics of the corresponding group and the common characteristics of its members. A group assignment module is used to assign users to one or more groups according to tags in the user tag library; The group maintenance and optimization module is used to regularly evaluate the effectiveness of group allocation, adjust group settings based on user feedback and behavior data, and continuously optimize the SVM model to optimize user labels and group configurations.

8. The tag-based user group management system according to claim 7, characterized in that: The system also includes a feedback loop mechanism and a visualization tool; Among them, the feedback loop mechanism is used to collect user response data to information push, and optimize the label generation group allocation strategy based on the corresponding data; Visualization tools are used to display the distribution of user tags, group characteristics, and the effects of group management, helping administrators make decisions; The data collection and preprocessing module also has data preprocessing functions, which are used to clean, standardize and normalize the collected data to improve data quality; The tag generator uses the support vector machine algorithm in the machine learning algorithm to identify and predict the user's hidden tags; When assigning users to groups, the group assignment module balances the size of the groups and the diversity of users within the groups, ensuring that the user labels within each group are representative and evenly distributed.

9. An electronic device, characterized in that: include: memory and at least one processor; Wherein, the memory stores a computer program; The at least one processor executes the computer program stored in the memory, so that the at least one processor performs the tag-based user group management method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which can be executed by a processor to implement the tag-based user group management method according to any one of claims 1 to 6.

Images