A User-oriented Contents Recommendation System in Peer-to-Peer Architecture

Recommender system is a popular technique for reducing information overload and finding digital contents that is most valuable to users. However, most recommender systems are based on a centralized client-server architecture in which servers and clients represents contents providers and users respectively. The existing recommender systems depend on contents providers and give a number of disadvantages to users. Therefore, we propose a recommender system based on a distributed P2P architecture that has originated with user-oriented principle rather than business itself. The proposed system consists of fully functioning personal recommender agents that automatically select neighbors and recommend contents. The agents learn user preference from users’ content usage without requiring users’ explicit ratings. We believe that the suggested P2P based recommender system should provide the users with more qualified recommendations, while it reduces the effort and time of users.