Technical Report CS-2007-37 Recommender Schemes for Peer-to-Peer Systems

In Peer-to-Peer (P2P) file sharing systems, peers spend a significant amount of time looking for relevant and interesting files. However, the files available for download represent on one hand a rich collection for different needs and preferences and on the other hand a struggle for the peers to find files that they like. In this paper, we propose new recommender schemes based on collaborative filtering. Peers collaborate to filter out irrelevant files. These schemes help peers find and discover new, interesting and relevant files. We propose recommender schemes based on Files’ Popularity and/or Peers’ Similarity. To overcome the problems of traditional collaborative filtering recommender systems, an implicit rating approach is used. Simulation results confirm the effectiveness of Peers’ Similarity based Recommendation in providing accurate recommendations. In addition, the proposed recommender schemes are proactive. Recommendations are provided to peers to motivate them to download the recommended files.