Information Filtering via Self-Consistent Refinement

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dc.creatorRen, Jie
dc.creatorZhou, Tao
dc.creatorZhang, Yi-Cheng
dc.date2008-02-26
dc.date.accessioned2026-07-07T09:43:12Z
dc.date.available2026-07-07T09:43:12Z
dc.descriptionRecommender 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.
dc.description4 pages, 2 figures
dc.identifierhttps://arxiv.org/abs/0802.3748
dc.identifierhttp://arxiv.org/abs/0802.3748
dc.identifierEPL 82 (2008) 58007
dc.identifierdoi:10.1209/0295-5075/82/58007
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/162466
dc.subjectData Analysis, Statistics and Probability
dc.subjectPhysics and Society
dc.titleInformation Filtering via Self-Consistent Refinement
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