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In this paper, the authors propose a Bayesian-inference based recommendation system for online social networks. In their 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. They develop distributed protocols that can be easily implemented in online social networks. They further propose to use Prior distribution to cope with cold start and rating sparseness.
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