Information filtering via self-consistent refinement

¹  Department of Physics, University of Fribourg - Chemin du Muse 3, 1700 Fribourg, Switzerland
²  Department of Physics and Centre for Computational Science and Engineering, National University of Singapore - Singapore 117542, Republic of Singapore
³  Department of Modern Physics, University of Science and Technology of China - Hefei 230026, PRC
⁴  Information Economy and Internet Research Laboratory, University of Electronic Science and Technology of China - Chengdu 610054, PRC

Corresponding author: zhutou@ustc.edu

Received: 29 February 2008
Accepted: 15 April 2008

Abstract

Recommender systems are significant to help people deal with the world of information explosion and overload. In this letter, we develop a general framework named self-consistent refinement and implement it by embedding two representative recommendation algorithms: similarity-based and spectrum-based methods. Numerical simulations on a benchmark data set demonstrate that the present method converges fast and can provide quite better performance than the standard methods.