Exploiting Ratings, Reviews and Relationships for Item Recommendations in Topic Based Social Networks

Many e-commerce platforms today allow users to give their rating scores and reviews on items as well as to establish social relationships with other users. As a result, such platforms accumulate heterogeneous data including numeric scores, short textual reviews, and social relationships. However, many recommender systems only consider historical user feedbacks in modeling user preferences. More specifically, most existing recommendation approaches only use rating scores but ignore reviews and social relationships in the user-generated data. In this paper, we propose TSNPF-a latent factor model to effectively capture user preferences and item features. Employing Poisson factorization, TSNPF fully exploits the wealth of information in rating scores, review text and social relationships altogether. It extracts topics of items and users from the review text and makes use of similarities between user pairs with social relationships, which results in a comprehensive understanding of user preferences. Experimental results on real-world datasets demonstrate that our TSNPF approach is highly effective at recommending items to users.