  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.
