A Self-Organized Personalized Recommendation System Based on Peer-to-Peer Networks

Personalized recommendation systems can help people to find interesting things and they are widely used in the world. Hybrid peer-to-peer recommendation systems intend to resolve problems of C/S recommendation systems by distributing items and calculating missions to all users. This paper presents our recent research work on the new self-organized personalized recommendation system based on pure peer-topeer network, it is also a multi-agent system, which contains lots of agents work in their peer, share and recommend automatic documents with the other agents. From various perspectives, our work focuses on how to adapt the system for a hybrid pure P2P network and implement users’ inside actions. Several models are proposed to make our system recommending effectively. These models are then evaluated and validated through implements and analyses. The results show some advantages of the proposed approach for the hybrid filtering based on recommendation threshold instead of Top-N and recommendation with Authority.