Top-N recommendation through belief propagation

Jiwoon Ha, Soon Hyoung Kwon, Sang Wook Kim, Christos Faloutsos, Sunju Park

Research output: Chapter in Book/Report/Conference proceedingConference contribution

26 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationCIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management
Number of pages4
Publication statusPublished - 2012
Event21st ACM International Conference on Information and Knowledge Management, CIKM 2012 - Maui, HI, United States
Duration: 2012 Oct 292012 Nov 2

Publication series

NameACM International Conference Proceeding Series


Other21st ACM International Conference on Information and Knowledge Management, CIKM 2012
Country/TerritoryUnited States
CityMaui, HI

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


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