A collaborative filtering recommendation methodology for peer-to-peer systems

Authors:
Hyea Kyeong Kim;Jae Kyeong Kim;Yoon Ho Cho
Affiliations:
School of Business Administration, KyungHee University, Seoul, Korea;School of Business Administration, KyungHee University, Seoul, Korea;School of E-Business, KookMin University, Seoul, Korea
Venue:
EC-Web'05 Proceedings of the 6th international conference on E-Commerce and Web Technologies
Year:
2005

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Abstract

To deal with the image recommending problems in P2P systems, this paper proposes a PeerCF-CB (Peer oriented Collaborative Filtering recommendation methodology using Contents-Based filtering). PeerCF-CB uses recent ratings of peers to adopt a change in peer preferences, and searches for nearest peers with similar preference through peer-based local information only. The performance of PeerCF-CB is evaluated with real transaction data in S content provider. Our experimental result shows that PeerCF-CB offers not only remarkably higher quality of recommendations but also dramatically faster performance than the centralized collaborative filtering recommendation systems.