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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Kim, H. K., Kim, J. K., & Cho, Y. H. (2005). A collaborative filtering recommendation methodology for peer-to-peer systems. In Lecture Notes in Computer Science (Vol. 3590, pp. 98–107). Springer Verlag. https://doi.org/10.1007/11545163_10
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