To address the issues of cold start,dilution,time-weighted relevance,and privacy protection in tag-based e-book recommendation algorithms,we design a comprehensive multi-label collaborative filtering recommendation algorithm for e-book resources.This algorithm employs multi-dimensional similarity calculations,integrating the similarity between users'own tags and resource tags,to enhance the accuracy of recommendations and provide users with more precise e-books that suit their interests.Additionally,a differential privacy protection mechanism is introduced to effectively safeguard user privacy.Experimental results demonstrate the algorithm's outstanding performance in terms of precision,recall,and F1 score,offering an efficient and privacy-preserving solution for library resource recommendations.
维普
万方数据