TY - GEN
T1 - Bayesian-inference based recommendation in online social networks
AU - Yang, Xiwang
AU - Guo, Yang
AU - Liu, Yong
PY - 2011
Y1 - 2011
N2 - In this paper, we propose a Bayesian-inference based recommendation system for online social networks. In our system, users share their movie 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 movie 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. The proposed algorithm is evaluated in a synthesized social network derived from a movie rating data set of real users. We show that the Bayesian-inference based recommendation provides personalized recommendations as accurate as the traditional CF approaches, and allows the flexible trade-offs between recommendation quality and recommendation quantity.
AB - In this paper, we propose a Bayesian-inference based recommendation system for online social networks. In our system, users share their movie 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 movie 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. The proposed algorithm is evaluated in a synthesized social network derived from a movie rating data set of real users. We show that the Bayesian-inference based recommendation provides personalized recommendations as accurate as the traditional CF approaches, and allows the flexible trade-offs between recommendation quality and recommendation quantity.
UR - http://www.scopus.com/inward/record.url?scp=79960867244&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79960867244&partnerID=8YFLogxK
U2 - 10.1109/INFCOM.2011.5935224
DO - 10.1109/INFCOM.2011.5935224
M3 - Conference contribution
AN - SCOPUS:79960867244
SN - 9781424499212
T3 - Proceedings - IEEE INFOCOM
SP - 551
EP - 555
BT - 2011 Proceedings IEEE INFOCOM
T2 - IEEE INFOCOM 2011
Y2 - 10 April 2011 through 15 April 2011
ER -