BOOK RECOMMENDATION SYSTEM BASED ON OPINION LEADERS AND POPULAR BOOKS
When readers borrow books from libraries or purchase books from shops,both celebrity recommended books and best seller list have a big influence to the selection of readers.In view of this,we propose a new book collaborative filtering recommendation system,which combines influence analysis and topic model.This algorithm combined maximum entropy and maximum variance to select the influential users and influential items in the rating matrix,and it predicted the unknown ratings based on the dense matrix.The algorithm applied enhanced clustering algorithm to cluster the word vectors,as a result,the topics of texts were constructed.Validation experiments were carried on public datasets.The results show that the proposed algorithm improves the performance of book recommendation system.
Book recommendation systemTopic modelSpherical k-means clusteringMaximum entropyCollab-orative filteringOpinion leader
国家科技期刊平台
NSTL
万方数据
维普
