Information Filtering via Self-Consistent Refinement

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 be 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.

4 pages, 2 figures

References in corpus (3)

• Heat Conduction Process on Community Networks as a Recommendation Model
• Effect of initial configuration on network-based recommendation
• Recommendation model based on opinion diffusion