Top-N recommendation through belief propagation

Authors:
Jiwoon Ha;Soon-Hyoung Kwon;Sang-Wook Kim;Christos Faloutsos;Sunju Park
Affiliations:
Hanyang University, Seoul, South Korea;Hanyang University, Seoul, South Korea;Hanyang University, Seoul, South Korea;Carnegie Mellon University, Pittsburgh, PA, USA;Yonsei University, Seoul, South Korea
Venue:
Proceedings of the 21st ACM international conference on Information and knowledge management
Year:
2012

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Abstract

The top-n recommendation focuses on finding the top-n items that the target user is likely to purchase rather than predicting his/her ratings on individual items. In this paper, we propose a novel method that provides top-n recommendation by probabilistically determining the target user's preference on items. This method models the purchasing relationships between users and items as a bipartite graph and employs Belief Propagation to compute the preference of the target user on items. We analyze the proposed method in detail by examining the changes in recommendation accuracy under different parameter settings. We also show that the proposed method is up to 40% more accurate than an existing method by comparing it with an RWR-based method via extensive experiments.