P2Prec: a Recommendation Service for P2P Content Sharing Systems

National audienceIn this paper, we propose P2Prec, a recommendation service for P2P content sharing systems that exploits users' social data. The key idea is to recommend to a user high quality documents in a specific topic using ratings of friends (or friends of friends) who are expert in that topic. To manage users' social data, we rely on Friend-Of-A-Friend (FOAF) descriptions. P2Prec has a hybrid P2P architecture to work on top of any P2P content sharing system. It combines efficient DHT indexing to manage the users' FOAF files with gossip robustness to disseminate the topics of expertise between friends. In our experimental evaluation, using the CiteSeer dataset, we show that P2Prec has the ability to get the maximum recall with very good performance. Furthermore, it increases recall and precision by a factor of 2 compared with centralized solutions