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.
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Kim, H.K., Kim, J.K., Cho, Y.H. (2005). A Collaborative Filtering Recommendation Methodology for Peer-to-Peer Systems. In: Bauknecht, K., Pröll, B., Werthner, H. (eds) E-Commerce and Web Technologies. EC-Web 2005. Lecture Notes in Computer Science, vol 3590. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11545163_10
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DOI: https://doi.org/10.1007/11545163_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28467-3
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