Socially Enabled Preference Learning from Implicit Feedback Data

In the age of information overload, collaborative filtering and recommender systems have become essential tools for content discovery. The advent of online social networks has added another approach to recommendation whereby the social network itself is used as a source for recommendations i.e. users are recommended items that are preferred by their friends. In this paper we develop a new model-based recommendation method that merges collaborative and social approaches and utilizes implicit feedback and the social graph data. Employing factor models, we represent each user profile as a mixture of his own and his friends’ profiles. This assumes and exploits “homophily” in the social network, a phenomenon that has been studied in the social sciences. We test our model on the Epinions data and on the Tuenti Places Recommendation data, a large-scale industry dataset, where it outperforms several state-of-the-art methods.