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In Peer-To-Peer (P2P) file sharing systems, peers spend a significant amount of time looking for relevant files. However, the files available for download represent on one hand a rich collection and on the other hand a struggle for the peers to find files that they like. In this paper, the authors propose "Asymmetric Peers' Similarity Based Recommendation with File Popularity" scheme that helps peers find and discover new and interesting files. To overcome the problems of traditional collaborative filtering recommender systems, an implicit rating approach is used. Simulation results confirm the effectiveness of the proposed scheme in providing accurate recommendations.
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