Social recommendation via multi-view user preference learning

Recommender system (RS) has become an active research area driven by the enormous industrial demands. Meanwhile, with the rapid development of microblogging system, various kinds of social data are available, which provide opportunities as well as challenges for traditional RSs. In this paper, we introduce the social recommendation (SR) problem utilizing microblogging data. We study this problem via multi-view user preference learning. Specifically, we first model user preference by learning a low-dimensional common representation of multi-view information including rating information, social relations, item side information, tagging information, and then recommend items based on the learnt user preference. We also develop an efficient alternating direction method of multipliers (ADMM) scheme to solve the proposed model. We empirically evaluate our approach using two real world datasets to demonstrate the significant improvement of our proposed approach against the state-of-the-art algorithms.