A Bayesian Treatment of Social Links in Recommender Systems

Recommender systems are increasingly driving user experiences on the Internet. This personalization is often achieved through the factorization of a large but sparse observation matrix of user-item feedback signals. In instances where the user's social network is known, its inclusion can significantly improve recommendations for cold start users. There are numerous ways in which the network can be incorporated into a probabilistic graphical model. We propose and investigate two ways for including a social network, either as a Markov Random Field that describes a user similarity in the prior over user features, or an explicit model that treats social links as observations. State of the art performance is reported on the Flixster online social network dataset.