Social Bayesian personal ranking for missing data in implicit feedback recommendation

Abstract

Recommendation systems estimate user’s preference to suggest items that might be interesting for them. Recently, implicit feedback recommendation has been steadily receiving more attention because it can be collected on a larger scale with a much lower cost than explicit feedback. The typical methods for recommendation are not well-designed for implicit feedback recommendation. Some effective methods have been proposed to improve implicit feedback recommendation, but most of them suffer from the problems of data sparsity and usually ignore the missing data in implicit feedback. Recent studies illustrate that social information can help resolve these issues. Towards this end, we propose a joint factorization model under the BPR framework utilizing social information. Remarkable, the experimental results show that our method performs much better than the state-of-the-art approaches and is capable of solving implicit problems, which indicates the importance of incorporating social information in the recommendation process to address the poor prediction accuracy.