关键词: 社会网络, 推荐系统, 概率矩阵分解, 信任关系

Abstract: Research suggests that adding explicit social trust to social network recommendations significantly improves the predictive accuracy of the scoring, but it is difficult to get trust score of users in real life. Previously, some scholars have studied and proposed a trust measurement method to calculate and predict the interaction between users and trust score. In this paper, a method of social trust relationship extraction based on Hellinger distance is proposed, and the similarity calculation is carried out by describing the f-divergence of one side node in binary network. Then, a new probability matrix factorization algorithm（CH-PMF） based on user group and implicit social relation is proposed by adding the hidden information to the improved probability matrix. Experimental results show that the proposed model has almost the same performance as the actual result of the actual trust score expressed by users, and CH-PMF has a better recommendation than other traditional algorithms when the trust data can not be extracted.

Key words: social network, recommendation system, probability matrix factorization, trust relationship