Personalized Recommendations in Peer-to-Peer Systems

In peer-to-peer (P2P) file sharing systems, peers have to choose the files of interest from a very large and rich collection of files. This task is difficult and time consuming. To alleviate the peers from the burden of manually looking for relevant files, recommender systems are used to make personalized recommendations to the peers according to their profile. In this paper, we propose a novel recommender scheme based on peers´ similarity and weighted files´ popularity. Simulation results confirm the effectiveness of the symmetric peers´ similarity with weighted file popularity scheme in providing accurate recommendations, this way, increasing peers´ satisfaction and contribution since peers will be motivated to download the recommended files and serve other peers meanwhile.