关键词: 推荐系统, 协同过滤, 主题模型, 社会关系网络

Abstract: Collaborative Topic Regression（CTR） combines ideas of probabilistic matrix factorization and topic modeling for recommender systems, which has obtained successes in many application. But this model does not take into account the impact of user social relationships on user interests. In order to solve this problem, the probability link is incorporated in this algorithm to excavate the influence of social relation network on the latent structure for users, and it`s also in constraint on the object function. This paper proposes a novel hierarchical Bayesian model called User Social Relation incorporated into Collaborative Topic Regression（USRCTR）, which extends CTR by integrating the user-item feedback information, item content information, and social relation network. Experiments on the Lastfm dataset show that this model can achieve better prediction accuracy than several improved methods of CTR with lower empirical training time.

Key words: recommender systems, collaborative filtering, topic models, social relations network