Information filtering via self-consistent refinement

2008/02/26 by Jie Ren, Tao Zhou, Yicheng Zhang +1
Computer Science · Decision Sciences · Physics and Astronomy · #Advanced Bandit Algorithms Research #Advanced Data Compression Techniques #Recommender Systems and Techniques #physics.data-an #physics.soc-ph

paper · pdf · doi:10.1209/0295-5075/82/58007

published as EPL 82 (2008) 58007 · 4 pages, 2 figures

arxiv created 2008/02/26 · openalex publication_date 2008/05/30 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30

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.

Citations

• Evaluating collaborative filtering recommender systems
• Effect of initial configuration on network-based recommendation
• Heat Conduction Process on Community Networks as a Recommendation Model
• Uncovering collective listening habits and music genres in bipartite networks