%A ZHANG Jia, LIN Yaojin, LIN Menglei, LIU Jinghua, LI Huizong
%T Entropy-based collaborative filtering algorithm
%0 Journal Article
%D 2016
%J Journal of Shandong University(Engineering Science)
%R 10.6040/j.issn.1672-3961.2.2015.047
%P 43-50
%V 46
%N 2
%U {http://gxbwk.njournal.sdu.edu.cn/CN/abstract/article_1427.shtml}
%8 2016-04-20
%X In the recommender system, the recommended quality was restricted by the sparsity of user rating data. To solve this problem, a novel entropy-based collaborative filtering algorithm was proposed. First, the definition of user entropy was given to reflect the rating distribution of users and their rating tendency degree. Then, the method of large margin was introduced to calculate the margin distance, and the neighbor selection range was determined via combining both of the active users entropy and margin distance with other users. Finally, neighbors were obtained by making full of the user entropy and the similarity between users, which could degrade the influence of the sparse rating data. Experimental results on two data sets showed that the proposed algorithm could improve the recommended quality effectively.