Bayesian-inference based recommendation in online social networks

—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. We further propose to use Prior distribution to cope with cold start and rating sparseness. The proposed algorithm is evaluated using two different online rating data sets of real users. We show that the proposed Bayesian-inference based recommendation is more accurate than the traditional Collaborative Filtering (CF) recommendation and the existing trust-based recommendations. It allows t...

• Type: Journal
• Year: 2011
• Where: INFOCOM
• Authors: Xiwang Yang, Yang Guo, Yong Liu
• Keywords: Bayesian Inference, Communications, Conditional Probabilities, INFOCOM 2011, Online Social Networks