Impact of Peers´ Similarity on Recommendations in P2P Systems

In this paper, we propose a novel recommender framework for P2P file sharing systems. The proposed recommender system is based on user-based collaborative filtering technique. We take advantage from the partial search process used in partially decentralized systems to explore the relationships between peers. The proposed recommender system does not require any additional effort from the users since implicit rating is used. To measure the similarity between peers, we investigate similarity metrics that were proposed in other fields and adapt them to file sharing P2P systems. We analyze the impact of each similarity metric on the accuracy of the recommendations. Files´ recommendations will increase users´ satisfaction since they will receive recommendations on files that they prefer.