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

作者：

Highlights：

• 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.

摘要

•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.

论文关键词：Recommendation system,Collaborative filtering,Social network,Item contents,Matrix factorization,Tags

论文评审过程：Received 3 September 2012, Revised 27 March 2013, Accepted 4 April 2013, Available online 15 April 2013.

论文官网地址：https://doi.org/10.1016/j.dss.2013.04.002