Information Filtering via Self-Consistent Refinement

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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

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Data Analysis, Statistics and Probability, Physics and Society

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http://salesiana.dossiersoluciones.com/handle/123456789/162466

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/0802.3748
 http://arxiv.org/abs/0802.3748
 EPL 82 (2008) 58007
 doi:10.1209/0295-5075/82/58007

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arXiv