Polytechnic Institute of NYU
Tuesday Oct 26, 2010, 1:30-2:30PM, CS Conference Room
Abstract: Recommendation plays an increasingly important role in our daily life. In this project, we propose a Bayesian-inference based recommendation system for online social networks. In our system, users share their 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 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 existing trust-based recommendations. It allows the flexible trade-offs between recommendation quality and recommendation quantity. We further show that informative Prior distribution is indeed helpful to overcome cold start and rating sparseness.
Yong Liu graduated with Ph.D degree from ECE Dept. at University of Massachusetts, Amherst in 2002. He worked as a Postdoc in computer networks research group at UMass from February 2002 to February 2005. He joined ECE department of Polytechnic Institute of NYU as an assistant professor in March 2005. His current research interest includes: multimedia networking, online social networks, and high speed wireless networks.
More information about him is available at: http://eeweb.poly.edu/faculty/yongliu/