A novel distributed collaborative filtering algorithm and its implementation on P2P overlay network

Abstract

Collaborative filtering (CF) has proved to be one of the most effective information filtering techniques. However, as their calculation complexity increased quickly both in time and space when the record in user database increases, traditional centralized CF algorithms has suffered from their shortage in scalability. In this paper, we first propose a novel distributed CF algorithm called PipeCF through which we can do both the user database management and prediction task in a decentralized way. We then propose two novel approaches: significance refinement (SR) and unanimous amplification (UA), to further improve the scalability and prediction accuracy of PipeCF. Finally we give the algorithm framework and system architecture of the implementation of PipeCF on Peer-to-Peer (P2P) overlay network through distributed hash table (DHT) method, which is one of the most popular and effective routing algorithm in P2P. The experimental data show that our distributed CF algorithm has much better scalability than traditional centralized ones with comparable prediction efficiency and accuracy.