Abstract

Recommender systems are becoming tools of choice to select the online information relevant to a given user. Collaborative filtering is the most popu-lar approach to building recommender systems and has been successfully employed in many applica-tions. With the advent of online social networks,the social network based approach to recommenda-tion has emerged. This approach assumes a social network among users and makes recommendations for a user based on the ratings of the users who have direct or indirect social relations with the given user.  As one of their major benefits, social net-work based approaches have been shown to reduce the problems with cold start users. In this paper,we explore a model-based approach for recommen-dation in social networks, employing matrix fac-torization techniques.  Advancing previous work,we incorporate the mechanism of trust propagation into the model in a principled way. Trust propaga-tion has been shown to be a crucial phenomenon in the social sciences, in social network analysis and in trust-based recommendation. We have con-ducted experiments on two real life data sets. Our experiments demonstrate that modeling trust propa-gation leads to a substantial increase in recommen-dation accuracy, in particular for cold start users.