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.
@article{arxiv.0802.3748,
title = {Information Filtering via Self-Consistent Refinement},
author = {Jie Ren and Tao Zhou and Yi-Cheng Zhang},
journal= {arXiv preprint arXiv:0802.3748},
year = {2008}
}