Social network behavior analysis-based community precise operation system and method

Abstract

The invention belongs to the field of information systems, and particularly relates to a market promotion community accurate operation system based on social network behavior analysis. The system comprises a data acquisition module, a preprocessing module, a user behavior analysis module, a community member classification module, a precision marketing recommendation module, an information spreading optimization module, a community activeness evaluation module, a feedback optimization module and the like. Through two innovative algorithm formulas of a user interest modeling algorithm based on a topic model and a user influence evaluation algorithm based on a graph neural network, deep analysis and accurate classification of user behaviors are realized. Personalized recommendation can be provided for the user, an information dissemination strategy is optimized, the community activeness is evaluated in real time, the system is optimized according to feedback, and the market promotion effect and the community operation efficiency are effectively improved.

Description

Technical Field

[0001] The present invention relates to the fields of marketing and Internet technologies, and particularly to a precise operation system and method for a market promotion community based on social network behavior analysis. The system and method aim to achieve the precise operation of the market promotion community, improve the promotion effect and user participation, and provide strong support for the market promotion of enterprises and merchants in the social network environment through in-depth mining and analysis of social network user behavior data. Background Art

[0002] In today's digital age, social networks have become an indispensable part of people's lives and at the same time provide a broad space for market promotion for enterprises and merchants. More and more enterprises and merchants establish market promotion communities, such as WeChat groups, QQ groups, social media groups, etc., to interact with target customers and promote products or services.

[0003] However, there are many problems in the operation of current market promotion communities. On the one hand, most community operators lack in-depth understanding of the behaviors of community members and can only carry out extensive promotion, unable to conduct precise marketing according to the personalized needs and interests of members, resulting in poor promotion effects and low user participation. On the other hand, the information dissemination efficiency within the community is low, and a large amount of invalid information floods in, making it difficult for valuable promotion information to be effectively conveyed to target users. In addition, the lack of an effective user feedback mechanism and community activity evaluation method also makes it difficult for community operators to evaluate and adjust the community operation effect in a timely and accurate manner.

[0004] Therefore, it is of great practical significance and market demand to develop a system and method that can precisely analyze social network user behaviors and achieve the precise operation of market promotion communities accordingly. Summary of the Invention

[0005] Object of the Invention

[0006] The object of the present invention is to provide a precise operation system and method for a market promotion community based on social network behavior analysis. By comprehensively and deeply analyzing the behavior data of social network users within the community, mining the interest preferences, consumption habits and demand characteristics of users, realizing the precise classification and personalized marketing of community members, improving the effect of market promotion and user participation, and at the same time optimizing the community information dissemination mechanism to enhance the community operation efficiency and activity.

[0007] System Overview

[0008] The precise operation system for a market promotion community based on social network behavior analysis of the present invention mainly includes the following modules:

[0009] 1. Data Acquisition Module

[0010] This module is responsible for collecting the behavioral data of users in the marketing community from social network platforms, including but not limited to information such as the content of users' speeches, speech time, likes, comments, shares, browsing records, and the time of joining the community. The collected data will serve as the basis for subsequent analysis.

[0011] 2. Data Preprocessing Module

[0012] Perform preprocessing operations such as cleaning, deduplication, and normalization on the collected raw data, remove noise data and invalid information, and convert the data into a format suitable for analysis. At the same time, perform word segmentation, part-of-speech tagging, sentiment analysis, etc. on the text data to extract the key information of the text.

[0013] 3. User Behavior Analysis Module

[0014] Apply machine learning and data analysis algorithms to deeply mine and analyze the preprocessed data. Specifically include:

[0015] · User Interest Modeling: By analyzing the content of users' speeches, browsing records, etc., construct a user interest model to understand the degree of users' interest in different topics and products.

[0016] · User Behavior Pattern Mining: Discover the behavioral rules and patterns of users in the community, such as speech frequency, active time periods, etc.

[0017] · User Influence Evaluation: Evaluate the influence of each user in the community, including the spread range of their speeches, the number of likes and comments received, etc.

[0018] 4. Community Member Classification Module

[0019] According to the results of user behavior analysis, use clustering algorithms to divide community members into different categories, such as loyal users, potential users, active users, silent users, etc. Develop different marketing strategies and operation plans for different categories of users.

[0020] 5. Precision Marketing Recommendation Module

[0021] Based on the user interest model and classification results, provide personalized product or service recommendations for each user. The recommended content can include product information, promotional activities, relevant articles, etc. At the same time, continuously optimize the recommendation strategy according to the user's behavior feedback to improve the accuracy and effectiveness of the recommendation.

[0022] 6. Information Dissemination Optimization Module

[0023] Analyze the paths and efficiency of information dissemination within the community. By optimizing the information release time, method, and content, improve the dissemination scope and reach of valuable promotional information. Meanwhile, utilize the results of user influence assessment to select appropriate users as nodes for information dissemination and expand the information dissemination effect.

[0024] 7. Community Activity Assessment Module

[0025] Establish a community activity assessment index system, comprehensively consider factors such as the number of user speeches, interaction frequency, and participation in activities, and conduct real-time assessment of the community activity. According to the assessment results, timely adjust the operation strategy to improve the community activity and user stickiness.

[0026] 8. Feedback and Optimization Module

[0027] Collect feedback from users on promotional content and operation strategies, and optimize and adjust the system's models and strategies through data analysis and machine learning algorithms to continuously improve the system's performance and operation effect.

[0028] Detailed Introduction to Innovative Algorithms

[0029] 1. User Interest Modeling Algorithm Based on Topic Model

[0030] Traditional user interest modeling methods often only focus on the surface behavior of users and are difficult to accurately capture the deep interests of users. The user interest modeling algorithm based on topic model proposed in the present invention uses the Latent Dirichlet Allocation (LDA) topic model to mine the topics of the user's speech content and convert the text data into a topic distribution vector. By analyzing the probability distribution of users on different topics, a user interest model is constructed. The specific steps are as follows:

[0031] ● Preprocess the user's speech content, including operations such as word segmentation and stop word removal.

[0032] ● Use the LDA model to train the preprocessed text data to obtain the topic distribution of each document and the word distribution of each topic.

[0033] ● According to the topic distribution of the user's speech documents, calculate the user's interest scores on each topic, thereby constructing a user interest model.

[0034] 2. User Influence Assessment Algorithm Based on Graph Neural Network

[0035] In a social network, the relationships between users form a complex graph structure. Traditional methods for evaluating user influence often overlook the relationship network among users. The user influence evaluation algorithm based on graph neural network proposed in this invention represents users in the social network and their interaction relationships as a graph structure, and learns the feature representations of nodes (users) and the relationships between nodes through a graph neural network (GNN), thereby evaluating user influence.

[0036] The specific steps are as follows:

[0037] ● Construct a graph structure of the social network, where nodes represent users and edges represent the interaction relationships between users (such as likes, comments, shares, etc.).

[0038] · Assign corresponding features to each node and edge, such as the basic information of users, the frequency and time of interactions, etc.

[0039] · Use a graph neural network (such as Graph Convolutional Network, GCN) to train the graph structure and learn the feature representations of nodes.

[0040] · Calculate the influence score of users based on the feature representations of nodes.

[0041] System Workflow

[0042] 1. Data Collection and Preprocessing Phase

[0043] · The data collection module collects the behavior data of users in the market promotion community from the social network platform.

[0044] ● The data preprocessing module performs preprocessing operations such as cleaning, deduplication, and normalization on the collected raw data, and extracts the key information of the text.

[0045] 2. User Behavior Analysis and Community Member Classification Phase

[0046] ● The user behavior analysis module uses machine learning and data analysis algorithms to deeply mine and analyze the preprocessed data, construct an interest model of users, mine user behavior patterns, and evaluate user influence.

[0047] · The community member classification module divides community members into different categories using a clustering algorithm based on the results of user behavior analysis.

[0048] 3. Precision Marketing and Information Dissemination Optimization Phase

[0049] ● The precision marketing recommendation module provides personalized product or service recommendations for each user based on the interest model and classification results of users.

[0050] ● The information dissemination optimization module analyzes the paths and efficiency of information dissemination within the community, optimizes the information release strategy, and improves the dissemination effect of promotional information.

[0051] 4. Community Activity Evaluation and Feedback Optimization Stage

[0052] ● The community activity evaluation module evaluates the activity of the community in real time and adjusts the operation strategy according to the evaluation results.

[0053] · The feedback and optimization module collects the feedback opinions of users and optimizes and adjusts the system model and strategy.

[0054] Beneficial Effects

[0055] The market promotion community precise operation system and method based on social network behavior analysis of the present invention have the following

[0056] Beneficial effects:

[0057] 1. Improve the market promotion effect

[0058] Through the precise classification and personalized marketing of community members, promotional information can be accurately conveyed to target users, increasing users' attention and participation in promotional content, thereby significantly improving the effect and conversion rate of market promotion.

[0059] 2. Optimize community information dissemination

[0060] By analyzing the paths and efficiency of information dissemination within the community, optimizing the information release strategy, and selecting appropriate information dissemination nodes, the dissemination range and reach of valuable promotional information can be increased, interference from invalid information can be reduced, and the efficiency of community information dissemination can be improved.

[0061] 3. Enhance community activity and user stickiness

[0062] By evaluating the activity of the community in real time, adjusting the operation strategy in a timely manner, and providing users with personalized services and experiences, the sense of identity and belonging of users to the community can be enhanced, and the activity and user stickiness of the community can be improved.

[0063] 4. Provide support for enterprise decision-making

[0064] Through in-depth analysis of user behavior data, the system can provide valuable market insights and decision-making basis for enterprises and merchants, helping enterprises better understand the needs of target customers and market trends, optimize products and services, and formulate more effective marketing strategies. Description of the Drawings Figure 1 It is the working flow chart of the operation system. Specific Embodiments

[0065] Example 1:

[0066] A marketing community precision operation system based on social network behavior analysis, including:

[0067] A data collection module is used to collect behavioral data of users in marketing communities from social networking platforms. The behavioral data includes but is not limited to user speech content, speech time, likes, comments, shares, browsing history, and community joining time;

[0068] The data preprocessing module performs preprocessing operations such as cleaning, deduplication, and normalization on the collected raw data, and performs word segmentation, part-of-speech tagging, and sentiment analysis on the text data;

[0069] The user behavior analysis module uses machine learning and data analysis algorithms to conduct in-depth mining and analysis of pre-processed data, build user interest models, explore user behavior patterns, and evaluate user influence;

[0070] The community member classification module uses a clustering algorithm to divide community members into different categories based on the results of user behavior analysis;

[0071] Precision marketing recommendation module, which provides personalized product or service recommendations for each user based on the user's interest model and classification results;

[0072] Information dissemination optimization module: analyzes the path and efficiency of information dissemination within the community, optimizes information release strategies, and improves the dissemination effect of promotional information;

[0073] Community activity evaluation module: establishes a community activity evaluation indicator system and conducts real-time evaluation of community activity;

[0074] The feedback and optimization module collects user feedback and optimizes and adjusts the system's models and strategies. The system according to claim 1, wherein the user behavior analysis module constructs a user interest model using an algorithm based on a topic model, which includes the following steps:

[0075] Preprocess the user's speech content to obtain a bag-of-words representation;

[0076] The preprocessed text data is trained using the Latent Dirichlet Allocation (LDA) topic model, and the topic distribution of each document d obtained by training is θ d , the word distribution of each topic t is φ t ; According to the topic distribution of the user's speech documents, calculate the user's interest score Iu,t on each topic. The calculation formula is Where Du is the speech document set of user u, θ d，tLet \(P(d|t)\) be the probability that document \(d\) belongs to topic \(t\), and \(w_d\) be the weight of document \(d\), thus constructing the user interest model. Among them, the user behavior analysis module evaluates user influence using an algorithm based on a graph neural network, and this algorithm includes the following steps:

[0077] Construct the graph structure \(G=(V, E)\) of the social network, where the nodes \(V\) represent users, and the edges \(E\) represent the interaction relationships between users;

[0078] Assign corresponding features to each node and edge, and use a graph neural network (GNN) to train the graph structure to learn the feature representation \(h_v\) of the nodes;

[0079] According to the feature representation of the nodes, calculate the influence score \(S_v\) of the user, and the calculation formula is \(S\) v =\(\sum\) u∈N(v) \(\alpha\) u，v \(\times h\) u , where \(N(v)\) is the set of neighbor nodes of node \(v\), \(a_{u,v}\) is the influence weight of node \(u\) on node \(v\), which is calculated through an attention mechanism, \(a\) is a learnable attention vector, and \(W\) is a learnable weight matrix. The community member classification module uses the K-Means clustering algorithm to divide community members into loyal users, potential users, active users, and silent users according to features such as users' interest scores, behavior patterns, and influence scores. The precise marketing recommendation module uses a combination of collaborative filtering algorithms and content recommendation algorithms to provide users with personalized product or service recommendations, and continuously optimizes the recommendation strategy according to users' behavior feedback. The information dissemination optimization module uses graph theory algorithms to calculate the shortest path and dissemination range of information dissemination, optimizes the information release time, method, and content according to the analysis results, and selects users with greater influence as information dissemination nodes. The community activity evaluation module uses a sliding window method to evaluate the community activity in real time, and adjusts the operation strategy in a timely manner according to the evaluation results. The feedback and optimization module collects users' feedback opinions through questionnaires, online messages, etc., and uses machine learning algorithms to optimize and adjust the system's models and strategies. The data collection module uses Python to write a crawler program and collects data in combination with the API interface provided by the social network platform. The data preprocessing module uses Python's NLTK library and Jieba library to perform operations such as word segmentation, stop word removal, and part-of-speech tagging on text data, and uses a pre-trained sentiment analysis model for sentiment analysis.

[0080] Data Collection and Preprocessing

[0081] Data Collection

[0082] Write a crawler program in Python, combined with the API interface provided by social network platforms, to collect users' behavior data from WeChat groups, QQ groups, and social media groups. The collected data includes information such as users' speech content, speech time, likes, comments, shares, browsing records, and the time of joining the community, and store the data in a database.

[0083] Data preprocessing

[0084] ● Data cleaning: Remove noise information in the data, such as duplicate records, garbled characters, advertising information, etc.

[0085] ● Text preprocessing: Perform operations such as word segmentation, stop word removal, and part-of-speech tagging on users' speech content, implemented using Python's NLTK library and Jieba library.

[0086] ● Sentiment analysis: Perform sentiment analysis on the text data to judge the sentiment tendency (positive, negative, neutral) of the text, using pre-trained sentiment analysis models such as SnowNLP or deep learning-based sentiment classification models.

[0087] Example 2: User behavior analysis and community member classification

[0088] User interest modeling is implemented using Python's Gensim library to build a user interest model based on the LDA topic model. The specific steps are as follows:

[0089] ● Preprocess the users' speech content to obtain a bag-of-words representation.

[0090] ● Train the LDA model, set the number of topics to 10, and the number of iterations to 100.

[0091] ● Calculate the users' interest scores on each topic according to the topic distribution of the users' speech documents, and build a user interest model.

[0092] User behavior pattern mining

[0093] Use time series analysis methods to analyze the users' speech frequency and active time periods. By counting the number of speeches of users in different time periods, discover the users' active patterns.

[0094] User influence assessment

[0095] Use Python's PyTorch library to implement a user influence assessment algorithm based on graph neural networks. The specific steps are as follows:

[0096] ● Build the graph structure of the social network and represent the graph using the NetworkX library.

[0097] ● Assign corresponding features to each node and edge, such as the number of user speeches, interaction frequency, etc.

[0098] · Define a graph neural network model, such as GCN, for training and evaluation.

[0099] · Calculate the influence score of the user based on the feature representation of the node.

[0100] Community member classification uses the K-Means clustering algorithm to divide community members into different categories. According to features such as the user's interest score, behavior pattern, and influence score, set the number of clusters to 4, and divide community members into loyal users, potential users, active users, and silent users.

[0101] Example 3: Precision marketing recommendation and information dissemination optimization

[0102] Precision marketing recommendation provides personalized product or service recommendations for each user based on the user's interest model and classification results. Use a combination of collaborative filtering algorithms and content recommendation algorithms. The recommended content includes product information, promotional activities, relevant articles, etc. At the same time, continuously optimize the recommendation strategy according to the user's behavior feedback.

[0103] Information dissemination optimization analyzes the path and efficiency of information dissemination within the community, and uses graph theory algorithms to calculate the shortest path and dissemination range of information dissemination. According to the analysis results, optimize the information release time, method, and content, and select users with greater influence as the nodes of information dissemination to improve the dissemination effect of promotional information.

[0104] Example 4: Community activity evaluation and feedback optimization

[0105] Community activity evaluation

[0106] Establish an evaluation index system for community activity, comprehensively consider factors such as the number of user speeches, interaction frequency, and participation in activities, and calculate the activity score of the community. Use the sliding window method to evaluate the community activity in real time, and adjust the operation strategy in a timely manner according to the evaluation results.

[0107] Feedback optimization

[0108] Collect the feedback opinions of users on the promotional content and operation strategy, and obtain the feedback information of users through questionnaires, online messages, etc. Analyze and process the feedback information, and use machine learning algorithms to optimize and adjust the system's model and strategy to continuously improve the system's performance and operation effect.

[0109] Due to document format limitations, the attached drawings are not provided here. In an actual patent application, drawings such as the overall architecture diagram of the system, the detailed structure diagrams of each module, and the flowcharts of algorithms can be drawn to more intuitively display the content of the invention. For example, draw the overall architecture diagram of the system to clearly show the relationships and data flows among various modules such as the data acquisition module, the data preprocessing module, and the user behavior analysis module; draw the flowchart of the user influence evaluation algorithm based on the graph neural network to detail the specific steps from constructing the graph structure to calculating the user influence score.

[0110] Example 5: Construction of Multi-dimensional User Portrait

[0111] Innovation Points of the Algorithm

[0112] Traditional user portraits are often constructed based on only partial user behavior data, with relatively single dimensions and difficult to comprehensively and accurately depict user characteristics. The multi-dimensional user portrait construction algorithm proposed in this example comprehensively considers various behavior data of users in the social network, including social relationships, interest preferences, consumption habits, emotional tendencies, etc., to construct a more comprehensive and detailed user portrait.

[0113] Modeling and Solving Process

[0114] 1. Data Integration: Integrate various types of user behavior data obtained by the data acquisition module, including users' social interaction data (such as friend relationships, group participation), speech content, browsing records, consumption records, etc.

[0115] 2. Feature Extraction: Extract multi-dimensional features from the integrated data, such as social influence features (such as the number of friends, the number of followers), interest features (interest scores based on topic models), consumption features (consumption amount, consumption frequency), emotional features (text emotional tendency), etc.

[0116] 3. Portrait Construction: Use principal component analysis (PCA) to perform dimensionality reduction on the extracted features to remove redundant information, and then divide users into different groups through a clustering algorithm (such as DBSCAN) to construct representative user portraits for each group.

[0117] Application Effect

[0118] Through the multi-dimensional user portrait, marketers and community operators can more deeply understand the characteristics and needs of different user groups and formulate more targeted marketing strategies. For example, for user groups with high consumption and positive emotions, promotion information on high-end products and high-quality services can be pushed; for user groups with low consumption and potential interests, personalized preferential activities and guiding content can be provided to improve the conversion rate and loyalty of users.

[0119] Example 6: Optimization of Real-time Information Push Strategy

[0120] Innovation Points of the Algorithm

[0121] Traditional information push strategies often push information regularly and in batches without considering the real-time status and needs of users, resulting in poor push effects. The real-time information push strategy optimization algorithm proposed in this embodiment combines the real-time behaviors and interest changes of users to dynamically adjust the timing, content, and method of information push, improving the accuracy and effectiveness of information push.

[0122] Modeling and Solving Process

[0123] 1. Real-time Behavior Monitoring: The data collection module is used to monitor the behaviors of users in the community in real time, such as speaking, liking, commenting, browsing, etc., to obtain the real-time status information of users.

[0124] 2. Dynamic Interest Update: According to the real-time behaviors of users, the incremental learning algorithm is used to dynamically update the interest model of users to ensure that the interest model can reflect the latest interests of users in a timely manner.

[0125] 3. Push Strategy Generation: Based on the real-time status and dynamic interest model of users, the reinforcement learning algorithm is used to generate the optimal information push strategy, including the selection of push time, push content, and determination of push method.

[0126] Application Effect

[0127] After adopting the real-time information push strategy optimization algorithm, the click-through rate and conversion rate of information push have increased significantly. Users can receive promotional information that matches their interests at the right time, improving the attention and participation of users in the promotional content. At the same time, it reduces the interference to users and improves the user experience.

[0128] Example 7: Prediction of Community Topic Popularity

[0129] Innovation Points of the Algorithm

[0130] Accurately predicting the popularity of topics in the community helps operators prepare for promotion in advance and improve the promotion effect. The community topic popularity prediction algorithm proposed in this embodiment combines time series analysis and machine learning methods, comprehensively considering factors such as the historical popularity of topics, the search frequency of relevant keywords, and the participation of users, to accurately predict the future popularity of topics.

[0131] Modeling and Solving Process

[0132] 1. Data Preparation: Collect the historical data of topics in the community, including the creation time of topics, the number of speeches, the number of likes, the number of comments, etc., and at the same time obtain the search frequency data of relevant keywords in the search engine.

[0133] 2. Feature engineering: Extract features from historical data, such as the initial popularity of a topic, the trend of popularity growth, the change rate of keyword search popularity, etc.

[0134] 3. Model training: Use a long short-term memory network (LSTM) to train the extracted features and build a topic popularity prediction model.

[0135] 4. Prediction and adjustment: Use the trained model to predict the future popularity of a topic and adjust and optimize the model according to the actual situation.

[0136] Application effect

[0137] Through the community topic popularity prediction algorithm, operators can understand in advance which topics will become popular topics, so as to formulate targeted promotion strategies. For example, launching relevant product or service promotion activities in a timely manner during the rising period of topic popularity can attract more users' attention and improve the promotion effect.

[0138] Example 8: Early warning of community user churn

[0139] Algorithm innovation points

[0140] Timely discovering potential churn users and taking corresponding retention measures are crucial for maintaining the stability and activity of the community. The community user churn early warning algorithm proposed in this example analyzes the changes in users' behavior patterns and the downward trend of participation to predict potential churn users in advance, providing decision-making support for operators.

[0141] Modeling and solution process

[0142] 1. Behavior pattern analysis: Analyze the historical behavior data of users to establish normal behavior patterns of users, including speech frequency, interaction time, participation in activities, etc.

[0143] 2. Anomaly detection: Monitor users' behaviors in real time and use the Isolation Forest algorithm to detect whether there are anomalies in users' behaviors, such as a sudden decrease in speech frequency or long-term non-participation in interactions.

[0144] 3. Churn warning: When anomalies in users' behaviors are detected, judge whether users have the risk of churn according to the preset threshold and issue a warning in a timely manner.

[0145] 4. Retention strategy formulation: For the warned users, formulate personalized retention strategies according to their historical behaviors and interest preferences, such as sending exclusive preferential information, inviting them to participate in special activities, etc.

[0146] Application effect

[0147] Through the community user churn warning algorithm, operators can timely identify potential users at risk of churn, take effective retention measures, reduce the user churn rate, and improve the stability and activity of the community. For example, for users who are warned due to lack of interaction for a period of time, sending a personalized greeting message and exclusive promotional activities may re-stimulate the users' enthusiasm for participation.

[0148] Example 9: Simulation of Community Influence Propagation

[0149] Algorithm Innovation Points

[0150] Understanding the propagation mechanism of information and influence within the community helps operators optimize promotion strategies and expand promotion effects. The community influence propagation simulation algorithm proposed in this example is based on complex network theory and Monte Carlo simulation method to simulate the information propagation process within the community and analyze the influence of different factors on influence propagation.

[0151] Modeling and Solving Process

[0152] 1. Community Network Modeling: Abstract the users within the community and their interaction relationships as a complex network, where nodes represent users, edges represent the interaction relationships between users, and the weight of the edge represents the intensity of the interaction.

[0153] 2. Definition of Propagation Rules: Define the rules for information propagation, such as the propagation probability, propagation range, propagation speed, etc. of the information, and consider the influence of factors such as the influence of users and interest preferences on propagation.

[0154] 3. Monte Carlo Simulation: Use the Monte Carlo simulation method to randomly simulate the information propagation process in the community network multiple times, and statistically calculate indicators such as the propagation range, depth, and speed.

[0155] 4. Result Analysis and Optimization: According to the simulation results, analyze the influence of different factors on influence propagation, such as the initial influence of users, the attractiveness of information, etc., so as to optimize promotion strategies and improve the effect of information propagation.

[0156] Application Effect

[0157] Through the community influence propagation simulation algorithm, operators can intuitively understand the information propagation process and effect within the community and predict the influence range of different promotion strategies. For example, through simulation, it is found that selecting users with higher influence as the starting point of information propagation can significantly expand the information propagation range, so these users are preferentially selected for cooperation in actual promotion.

[0158] Example 10: Cross-Community Collaborative Promotion Strategy

[0159] Algorithm Innovation Points

[0160] Collaborative promotion among multiple related communities can expand the coverage and influence of the promotion. The cross-community collaborative promotion strategy algorithm proposed in this embodiment formulates an optimal cross-community collaborative promotion strategy by analyzing the user characteristics, interest distribution, and influence relationship of different communities, so as to achieve resource sharing and complementary advantages.

[0161] Modeling and solving process

[0162] 1. Community feature analysis: Analyze the user behavior data of multiple related communities to understand the user characteristics, interest distribution, activity level, etc. of each community.

[0163] 2. Community relationship modeling: Establish a relationship model between different communities, considering factors such as user overlap, topic relevance, and influence propagation relationship between communities.

[0164] 3. Collaborative promotion strategy formulation: According to the results of community feature analysis and community relationship modeling, use the linear programming algorithm to formulate an optimal cross-community collaborative promotion strategy, including the selection of promotion content, the allocation of promotion channels, the arrangement of promotion time, etc.

[0165] 4. Strategy implementation and evaluation: Implement the formulated cross-community collaborative promotion strategy, and evaluate the promotion effect through data analysis. Adjust and optimize the strategy according to the evaluation results.

[0166] Application effect

[0167] After adopting the cross-community collaborative promotion strategy algorithm, the coverage and influence of the promotion activities have been significantly improved. Resource sharing and complementary advantages have been achieved between different communities, attracting the attention of more potential users and improving the effect and efficiency of market promotion. For example, through cross-community collaborative promotion, the promotion information of a beauty brand can cover multiple beauty-related communities at the same time, attracting more users interested in beauty, thereby increasing the brand's popularity and product sales.

Claims

1. A precise operation system for a market promotion community based on social network behavior analysis, characterized in that, Including:

1. A data collection module, which is used to collect the behavioral data of users in the market promotion community from the social network platform. The behavioral data includes but is not limited to user speech content, speech time, likes, comments, shares, browsing records, and community joining time.

2. A data preprocessing module, which performs preprocessing operations such as cleaning, deduplication, and normalization on the collected raw data, and performs word segmentation, part-of-speech tagging, and sentiment analysis on the text data.

3. A user behavior analysis module, which uses machine learning and data analysis algorithms to deeply mine and analyze the preprocessed data, construct a user interest model, mine user behavior patterns, and evaluate user influence.

4. A community member classification module, which divides community members into different categories using a clustering algorithm according to the results of user behavior analysis.

5. A precision marketing recommendation module, which provides personalized product or service recommendations for each user based on the user's interest model and classification results.

6. An information dissemination optimization module, which analyzes the path and efficiency of information dissemination in the community, optimizes the information release strategy, and improves the dissemination effect of promotional information.

7. A community activity evaluation module, which establishes an evaluation index system for community activity and evaluates the activity of the community in real time.

8. A feedback and optimization module, which collects user feedback and optimizes and adjusts the system's model and strategy.

2. The system according to claim 1, wherein the user behavior analysis module constructs a user interest model using an algorithm based on a topic model, and the algorithm includes the following steps:

1. Preprocess the user's speech content to obtain a bag-of-words representation.

2. Use the Latent Dirichlet Allocation (LDA) topic model to train the preprocessed text data, and assume that the topic distribution of each document d obtained by training is θ d , and the word distribution of each topic t is φ t ; 3. Calculate the user's interest score Iu,t in each topic according to the topic distribution of the user's speech documents. The calculation formula is where Du is the set of speech documents of user u, and θ d，t is the probability that document d belongs to topic t, and wd is the weight of document d, thus constructing the user's interest model.

3. The system according to claim 1, wherein the user behavior analysis module evaluates user influence using an algorithm based on a graph neural network, and the algorithm includes the following steps:

1. Construct a graph structure G=(V, E) of the social network, where the nodes V represent users and the edges E represent the interaction relationships between users.

2. Assign corresponding features to each node and edge, and use a graph neural network (GNN) to train the graph structure to learn the feature representation hv of the nodes.

3. Calculate the influence score Sv of the user according to the feature representation of the node. The calculation formula is S v = ∑ u∈N(v) α u，v × h u , where N(v) is the set of neighbor nodes of node v, au,v is the influence weight of node u on node v, which is calculated through the attention mechanism, a is a learnable attention vector, and W is a learnable weight matrix.

4. The system according to claim 1, characterized in that, The community member classification module uses the K-Means clustering algorithm to divide community members into loyal users, potential users, active users, and silent users according to features such as the user's interest score, behavior pattern, and influence score.

5. The system according to claim 1, wherein The precision marketing recommendation module uses a combination of collaborative filtering algorithm and content recommendation algorithm to provide personalized product or service recommendations for users, and continuously optimizes the recommendation strategy according to the user's behavior feedback.

6. The system according to claim 1, characterized in that The information dissemination optimization module uses graph theory algorithms to calculate the shortest path and dissemination range of information dissemination, optimizes the information release time, method, and content according to the analysis results, and selects users with greater influence as the nodes of information dissemination.

7. The system according to claim 1, wherein The community activity evaluation module uses a sliding window method to evaluate the community activity in real time, and adjusts the operation strategy in a timely manner according to the evaluation results.

8. The system according to claim 1, characterized in that, The feedback and optimization module collects user feedback through methods such as questionnaires and online messages, and uses machine learning algorithms to optimize and adjust the system's model and strategy.

9. The system according to claim 1, characterized in that, The data acquisition module uses Python to write a crawler program and collects data in combination with the API interface provided by the social network platform.

10. The system according to claim 1, wherein, The data preprocessing module uses the NLTK library and Jieba library of Python to perform operations such as word segmentation, stop word removal, and part-of-speech tagging on the text data, and uses a pre-trained sentiment analysis model for sentiment analysis.

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