Intelligent recommendation system based on user behavior data

Abstract

The invention relates to the field of recommendation systems, in particular to an intelligent recommendation system based on user behavior data, which comprises a data collection module for collecting behavior data of a user on a platform; the intelligent label generation module is used for generating interest labels based on the user behavior data; the data preprocessing module performs cleaning, normalization and dimension reduction processing on the collected data; the feature extraction and engineering module is used for extracting features from the preprocessed data and constructing feature vectors of the user and the article; and the model training module is used for training by utilizing a machine learning algorithm to generate a recommendation model. According to the method, the personalized recommendation result is provided by analyzing the behavior data of the user on the platform, so that the user experience and the satisfaction degree are improved. The system generates interest labels of users by collecting behaviors of clicking, browsing, searching, purchasing and the like of the users, and performs real-time recommendation based on the data.

Description

Technical Field

[0001] The present invention relates to the technical field of recommendation systems, and in particular to an intelligent recommendation system based on user behavior data. Background Art

[0002] The recommendation system aims to provide personalized recommendation results by analyzing user behavior data on the platform to improve user experience and satisfaction.

[0003] There are two common recommendation systems: one is the recommendation system based on collaborative filtering, and the other is the recommendation system based on content. Among them, the recommendation system based on collaborative filtering uses a collaborative filtering algorithm to generate recommendations by analyzing the similarities between users. For example, Amazon's recommendation system uses a user-based collaborative filtering method to find similar users and recommend the products they have purchased by comparing users' purchasing behaviors. The content-based recommendation system makes recommendations by analyzing the characteristics of items and users' interests. For example, Netflix's recommendation system recommends similar movies based on the types, actors and other characteristics of the movies that users have watched.

[0004] However, the recommendation system based on collaborative filtering has a cold start problem, that is, new users or new items lack sufficient behavioral data, resulting in the system being unable to generate effective recommendation results, and there is a sparsity problem, that is, the interaction data between users and items is often sparse, making it difficult for the recommendation algorithm to find sufficient similarities, thus affecting the accuracy of the recommendation; the recommendation system based on content has the problem of single interest, that is, the system usually makes recommendations based on the user's past behavior, which easily leads to the singleness of the recommended content, and the user may miss out on a variety of choices, and there is a problem of weak recommendation ability for new items, that is, due to the lack of user feedback, the system cannot generate recommendations in a timely manner for newly released items, affecting the user experience. Summary of the invention

[0005] The purpose of the present invention is to propose an intelligent recommendation system based on user behavior data to address the problems of cold start, sparsity, single interest and weak new item recommendation ability existing in the background technology.

[0006] The technical solution of the present invention: an intelligent recommendation system based on user behavior data, comprising:

[0007] Data collection module, which collects user behavior data on the platform;

[0008] Intelligent tag generation module, which generates interest tags based on user behavior data;

[0009] Data preprocessing module, which cleans, normalizes and reduces the dimension of the collected data;

[0010] Feature extraction and engineering module, extracts features from preprocessed data and constructs feature vectors for users and items;

[0011] And the model training module uses machine learning algorithms to train and generate recommendation models.

[0012] Preferably, it also includes a recommendation algorithm module, which uses a variety of recommendation algorithms to generate personalized recommendation results.

[0013] Preferably, the recommendation algorithm includes content-based recommendation, collaborative filtering and hybrid recommendation.

[0014] Preferably, the recommended algorithm calculation formula is as follows:

[0015]

[0016] Among them, R ij represents the recommendation score of item j by i, w ik represents the similarity between user i and other users k, r kj represents the rating of user k on item j.

[0017] Preferably, a real-time recommendation and update module is also included to analyze the latest user behavior in real time and dynamically update user tags and recommendation results.

[0018] Preferably, an evaluation and optimization module is also included to evaluate the recommendation model and continuously optimize the recommendation effect.

[0019] Compared with the prior art, the present invention has the following beneficial technical effects:

[0020] 1. Improved data security: The present invention uses encryption technology and access control mechanisms to ensure the security of user data during collection and storage, effectively prevent data leakage and abuse, enhance user trust in the platform, and improve user participation and activity;

[0021] 2. Accuracy of personalized recommendations: Through intelligent tag generation and high-quality feature extraction, the system can more accurately capture user preferences, significantly improve the accuracy of recommendation results, increase user satisfaction, and improve conversion rates;

[0022] 3. Resource saving: The system's real-time update mechanism reduces the waste of computing resources. By dynamically adjusting recommended content, invalid calculations are avoided, the server burden is reduced, computing resources and maintenance costs are saved, and the overall efficiency of the system is improved.

[0023] 4. Fast data processing speed: Adopt optimized data preprocessing and feature extraction processes to ensure efficient data processing, significantly shorten the response time from user request to recommendation results, and improve user experience;

[0024] 5. Flexible recommendation strategy: Combining multiple recommendation algorithms, the system can flexibly adjust the recommendation strategy according to different user behaviors and needs, improve the adaptability of the recommendation, meet the personalized needs of different users, and enhance the competitiveness of the platform;

[0025] 6. Continuous optimization capability: The system has a regular evaluation and optimization mechanism to adjust algorithm parameters in a timely manner to adapt to changes in user behavior, ensuring that the recommendation effect is always maintained at a high level, thereby improving the long-term satisfaction of users;

[0026] 7. Enhanced user stickiness: The combination of personalized recommendations and real-time updates enables users to experience content and products that are more in line with their needs, improves user return rates and stay time, promotes long-term use by users, and increases the user base of the platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A schematic diagram of the structure of an embodiment of the present invention;

[0028] Figure 2 This is a flow chart of the architecture of the intelligent recommendation system based on user behavior data proposed in the present invention. DETAILED DESCRIPTION

[0029] like Figure 1-Figure 2 As shown, the intelligent recommendation system based on user behavior data proposed by the present invention includes:

[0030] The data collection module collects user behavior data on the platform, such as clicks, browsing, searches, and purchases. Through comprehensive data collection, the system can identify users' real interests and needs, lay the foundation for personalized recommendations, and reduce recommendation bias.

[0031] The smart tag generation module generates interest tags based on user behavior data. These tags are presented in the form of keywords and categories. The generation of smart tags enables the recommendation system to more accurately locate user interests, thereby improving the relevance and personalization of recommendations.

[0032] The data preprocessing module cleans, normalizes and reduces the dimension of the collected data to improve data quality. High-quality data input directly affects the performance of subsequent models. The preprocessing process ensures the accuracy of the data and improves the efficiency of model training.

[0033] Feature extraction and engineering module extracts features from preprocessed data and constructs feature vectors of users and items. Rich feature vectors provide more comprehensive information support for machine learning models, enhancing the learning ability and recommendation effect of the models.

[0034] The model training module uses machine learning algorithms, such as matrix decomposition and collaborative filtering, to train and generate recommendation models;

[0035] The recommendation algorithm module uses a variety of recommendation algorithms to generate personalized recommendation results. The diversified algorithm design enables the recommendation system to better adapt to the needs of different users and improve the accuracy of recommendations and user experience. The recommendation algorithms include content-based recommendations, collaborative filtering, and hybrid recommendations. The recommendation algorithm calculation formula is as follows:

[0036]

[0037] Among them, R ij represents the recommendation score of item j by i, w ik represents the similarity between user i and other users k, r kj represents the rating of user k on item j

[0038] Real-time recommendation and update module, which is used to analyze the latest user behavior in real time, dynamically update user tags and recommendation results, and ensure that the recommended content is always consistent with the user's current interests, thereby improving the timeliness and relevance of recommendations and enhancing user stickiness;

[0039] As well as the evaluation and optimization module, it evaluates the recommendation model and uses indicators such as accuracy and recall rate to continuously optimize the recommendation effect. Through continuous evaluation and optimization, the system can adapt to changes in user needs, improve the accuracy of recommendations and user satisfaction, and ensure the long-term effectiveness of the system.

[0040] In summary, when the present invention is used, through comprehensive data collection, the system can identify the user's real interests and needs, lay the foundation for personalized recommendations, and reduce recommendation bias; the generation of intelligent tags enables the recommendation system to more accurately locate the user's interests, thereby improving the relevance and personalization of recommendations; high-quality data input directly affects the performance of subsequent models, and the preprocessing process ensures the accuracy of the data and improves the efficiency of model training; rich feature vectors provide more comprehensive information support for machine learning models, enhance the model's learning ability and recommendation effect; diversified algorithm design enables the recommendation system to better adapt to the needs of different users, improve the accuracy of recommendations and user experience; real-time updates ensure that the recommended content is always consistent with the user's current interests, improve the timeliness and relevance of recommendations, and enhance user stickiness; through continuous evaluation and optimization, the system can adapt to changes in user needs, improve the accuracy of recommendations and user satisfaction, and ensure the long-term effectiveness of the system.

[0041] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited thereto, and various changes can be made within the knowledge scope of technicians in the relevant technical field without departing from the purpose of the present invention.

Claims

1. Intelligent recommendation system based on user behavior data, characterized by: include: Data collection module, which collects user behavior data on the platform; Intelligent tag generation module, which generates interest tags based on user behavior data; Data preprocessing module, which cleans, normalizes and reduces the dimension of the collected data; Feature extraction and engineering module, extracts features from preprocessed data and constructs feature vectors for users and items; And the model training module uses machine learning algorithms to train and generate recommendation models.

2. The intelligent recommendation system based on user behavior data according to claim 1, characterized in that: It also includes a recommendation algorithm module, which uses a variety of recommendation algorithms to generate personalized recommendation results.

3. The intelligent recommendation system based on user behavior data according to claim 2 is characterized in that: Recommendation algorithms include content-based recommendation, collaborative filtering, and hybrid recommendation.

4. The intelligent recommendation system based on user behavior data according to claim 2, characterized in that: The recommended algorithm calculation formula is as follows: Among them, R ij represents the recommendation score of item j by i, w ik represents the similarity between user i and other users k, r kj represents the rating of user k on item j.

5. The intelligent recommendation system based on user behavior data according to claim 2, characterized in that: It also includes a real-time recommendation and update module, which is used to analyze the user's latest behavior in real time and dynamically update user tags and recommendation results.

6. The intelligent recommendation system based on user behavior data according to claim 5, characterized in that: It also includes an evaluation and optimization module to evaluate the recommendation model and continuously optimize the recommendation effect.

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