Evaluation of P2P information recommendation based on collaborative filtering

Abstract

Collaborative filtering is a social information recommendation/filtering method, and the peer-to-peer (P2P) computer network is a network on which information is distributed on the peer-to-peer basis (each peer node works as a server, a client, and even a router). This research aims to develop a model of P2P information recommendation system based on collaborative filtering and evaluate the ability of the system by computer simulations based on the model. We previously proposed a simple model, and the model in this paper is a modified one that is more focused on recommendation agents and user-agent interactions. We have developed a computer simulator program and tested simulations with several parameter settings. From the results of the simulations, recommendation recall and precision are evaluated. Findings are that the agents are likely to overly recommend so that the recall score becomes high but the precision score becomes low. © Springer-Verlag Berlin Heidelberg 2007.