A Peer-to-Peer Based Distributed Collaborative Filtering Architecture
Songjie Gong, HongWu Ye, Ping Su · 2009
Collaborative filtering recommendation algorithm has proved to be one of the most successful algorithms in recommender systems in recent years. However, traditional centralized collaborative filtering system has suffered from its shortage in scalability as their calculation complexity increases quickly both in time and space when the number of the user and item in the rating database increases. As a result, distributed collaborative filtering is attracting increasing attention as an alternative implementation scheme for collaborative filtering recommender systems. This paper proposes a distributed collaborative filtering architecture based on peer-to-peer networks, because of its advantage of scalability as an alternative architecture. The peers communicate each other by sending messages. The peer-to-peer application advantage is employed in order to manage the user-item rating database, produce the prediction and top-N recommendation.