A Matrix Factorization Recommendation Model Fusing Social Relationship

Abstract

Recommendation systems can effectively solve the problem of information overload and provide personalized recommendation service for users.However,traditional models which generate prediction results only by analyzing user project scoring matrix are not effective in the case of sparse scoring matrix.To address the problem,this paper uses the rating information and social trust relationships of users to calculate user similarity,and on this basis proposes a Matrix Factorization(MF) recommendation model named SoRegIM fusing social relationship.By mining the topological relationship of users in social network,the weighted social trust network is constructed based on the information of direct and indirect neighbors of the target users,so as to reduce the redundant social noise while making full use of the social relationship information of users.Experimental results on the open datasets show that,compared with classical models such as SoRec and SocialMF,SoRegIM has higher recommendation accuracy and demonstrates an obvious improvement on sparse data.