Bayesian Probabilistic Matrix Factorization with Social Relations and Item Contents for recommendation

• We modify the model of Bayesian Probabilistic Matrix Factorization.
• We fuse social relations and item contents with rating data in a novel way.
• The proposed method gets more accurate results in faster converge speed.
• The proposed method can alleviate data sparsity problem and cold-start problem.

Recommendation systems have received great attention for their commercial value in today's online business world. However, most recommendation systems encounter the data sparsity problem and the cold-start problem. To improve recommendation accuracy in this circumstance, additional sources of information about the users and items should be incorporated in recommendation systems. In this paper, we modify the model in Bayesian Probabilistic Matrix Factorization, and propose two recommendation approaches fusing social relations and item contents with user ratings in a novel way. The proposed approach is computationally efficient and can be applied to trust-aware or content-aware recommendation systems with very large dataset. Experimental results on three real world datasets show that our method gets more accurate recommendation results with faster converging speed than other matrix factorization based methods. We also verify our method in cold-start settings, and our method gets more accurate recommendation results than the compared approaches.