Biblio

Due to the large quantity and diversity of content being easily available to users, recommender systems (RS) have become an integral part of nearly every online system. They allow users to resolve the information overload problem by proactively generating high-quality personalized recommendations. Trust metrics help leverage preferences of similar users and have led to improved predictive accuracy which is why they have become an important consideration in the design of RSs. We argue that there are additional aspects of trust as a human notion, that can be integrated with collaborative filtering techniques to suggest to users items that they might like. In this paper, we present an approach for the top-N recommendation task that computes prediction scores for items as a user specific combination of global and local trust models to capture differences in preferences. Our experiments show that the proposed method improves upon the standard trust model and outperforms competing top-N recommendation approaches on real world data by upto 19%.