Probabilistic Matrix Factorization Recommendation Algorithm with User Trust Similarity

College of computer Science and Technology, Harbin Engineering University, Harbin, China

Abstract

In this paper, we describe the formatting guidelines for Conference Proceedings. Whether the user similarity calculation is reasonable in the traditional collaborative filtering recommendation algorithm directly affects the result of the collaborative filtering recommendation algorithm. This paper proposes a probabilistic matrix factorization recommendation algorithm with user trust similarity which combines improved similarity of users’ trust and probability matrix factorization recommendation method. The results show that proposed algorithm could relieve user cold start issues and effectively reduce the error of recommendation.