Library personalized reading recommendation system based on artificial intelligence

Through the library's personalized reading recommendation system based on artificial intelligence, the problems of insufficient accuracy and diversity of traditional library recommendation systems have been solved, personalized recommendations and resource optimization have been achieved, and user satisfaction and library resource utilization efficiency have been improved.

CN120596736APending Publication Date: 2025-09-05李红胜
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Patent Information

Application Number
CN202510668712.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-23
Publication Date
2025-09-05

AI Technical Summary

Technical Problem

Traditional library recommendation systems cannot accurately identify readers' unique interests and hobbies, lack recommendation diversity, find it difficult to adapt to the dynamic changes in readers' interests, and make insufficient use of data, resulting in waste of resources and low reading satisfaction.

Method used

The library adopts an AI-based personalized reading recommendation system, which generates personalized reading recommendations through data collection, preprocessing, AI model training and recommendation model generation, combined with user interest modeling and book feature extraction, and updates and optimizes the model in real time, providing diversity optimization and user feedback mechanisms.

Benefits of technology

It achieves accurate recommendations, improves users’ reading experience and library resource utilization, optimizes book purchasing decisions, and enhances the interactivity and trust between users and the system.

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Abstract

The invention discloses a library personalized reading recommendation system based on artificial intelligence, and the system comprises a data collection subsystem which is used for collecting the following data from a business system, a user interaction terminal and external network resources of a library; the data preprocessing module is connected with the data acquisition subsystem and is used for executing the following operations on the acquired data; and the artificial intelligence model training module is connected with the data preprocessing module. Accurate recommendation: through deep analysis of personal information, reading history, browsing behaviors, collection records and other multi-dimensional data of the user, an advanced artificial intelligence algorithm is utilized to construct a user interest model and a book feature model, and books meeting interest and reading requirements of the user can be accurately recommended to each user. Therefore, the user does not need to spend a large amount of time in screening in massive book resources and quickly find the interested books, the time cost is saved, and the pertinence and the satisfaction degree of reading are greatly improved.
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Citation Information

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