Optimizing Collaborative Filtering Recommendation Algorithms for Knowledge Sharing in Libraries

With the rapid development of information technology, how to enable teachers and students to quickly find and filter information of interest in massive collections has become a hot issue for many scholars. This paper enhances the traditional collaborative filtering algorithm by utilizing the knowledge-sharing model. Specifically, we calculate similarity using keyword information from items and dynamic information from users based on similarity calculations related to item features and user attributes. The relevant information about users and items is fully utilized to successfully alleviate the problem of new items and new users, and the entire process of collaborative filtering recommendations for libraries is designed. The improved collaborative filtering-based algorithm can achieve a recommendation accuracy of more than 60% and recommend more accurate books to users. The average recommendation rate of the book recommendation algorithm is 0.056 higher than the average recommendation rate of other recommender systems, indicating a higher recommendation rate that can better match the needs of users and alleviate the cold-start problem to a certain extent.