Matrix Factorization with Explicit Trust and Distrust Side Information for Improved Social Recommendation

With the advent of online social networks, recommender systems have became crucial for the success of many online applications/services due to their significance role in tailoring these appli-cations to user-specific needs or preferences. Despite their increasing popularity, in general recom-mender systems suffer from the data sparsity and the cold-start problems. To alleviate these issues, in recent years there has been an upsurge of interest in exploiting social information such as trust relations among users along with the rating data to improve the performance of recommender sys-tems. The main motivation for exploiting trust information in recommendation process stems from the observation that the ideas we are exposed to and the choices we make are significantly influ-enced by our social context. However, in large user communities, in addition to trust relations, the distrust relations also exist between users. For instance, in Epinions the concepts of personal "web of trust " and personal "block list " allow users to categorize their friends based on the quality of re-views into trusted and distrusted friends, respectively. Hence, it will be interesting to incorporate this new source of information in recommendation as well. In contrast to the incorporation of trust information in recommendation which is thriving, the potential of explicitly incorporating distrus