Volume 82, Number 5, June 2008
|Number of page(s)||5|
|Section||Interdisciplinary Physics and Related Areas of Science and Technology|
|Published online||30 May 2008|
Information filtering via self-consistent refinement
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: firstname.lastname@example.org
Accepted: 15 April 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.
PACS: 89.75.-k – Complex systems / 89.20.Hh – World Wide Web, Internet / 89.65.Gh – Economics; econophysics, financial markets, business and management
© EPLA, 2008
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.