Social Recommendation Model Regularized with User Trust and Item Ratings

Social trust aware recommender systems have been well studied in recent years. However, most of existing methods focus on the recommendation scenarios where users can provide explicit feedback to items. But in most cases, the feedback is not explicit but implicit. Moreover, most of trust aware methods assume the trust relationships among users are single and homogeneous, whereas trust as a social concept is intrinsically multi-faceted and heterogeneous. Simply exploiting the raw values of trust relations cannot get satisfactory results. Based on the above observations, we propose to learn a trust aware personalized ranking method with multi-faceted trust relations for implicit feedback. Recommender systems have been widely used to provide users with high-quality personalized recommendations from a large volume of choices. Robust and accurate recommendations are important in e-commerce operations (e. g. , navigating product offerings, personalization, improving customer satisfaction), and in marketing (e. g. , tailored advertising, segmentation, cross-selling). The former issue refers to the fact that users usually rate only a small portion of items while the latter indicates that new users only give a few ratings (a. k. a. cold-start users). Both issueseverely degrade the efficiency of a recommender system in modeling user preferences and thus the accuracy of predicting a users rating for an unknown item.