Information filtering via self-consistent refinementJie Ren1, 2, Tao Zhou1, 3, 4 and Yi-Cheng Zhang1, 4
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
received 29 February 2008; accepted in final form 15 April 2008; published June 2008
published online 30 May 2008
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.
89.75.-k - Complex systems.
89.20.Hh - World Wide Web, Internet.
89.65.Gh - Economics; econophysics, financial markets, business and management.
© EPLA 2008