In this paper, we propose a Bayesian inference-based recommendation system for online social networks. In our system,users share their content ratings with friends. The rating similarity between a pair of friends is measured by a set of conditional probabilities derived from their mutual rating history. A user propagates a content rating query along the social network to his direct and indirect friends. Based on the query responses, a Bayesian network is constructed to infer the rating of the querying user. We develop

distributed protocols that can be easily implemented in online social networks.