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Issue No.08 - August (2009 vol.42)

pp: 30-37

Yehuda Koren , Yahoo Research

Robert Bell , AT&T Labs

Chris Volinsky , AT&T Labs

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MC.2009.263

ABSTRACT

As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest-neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.

INDEX TERMS

Computational intelligence, Netflix Prize, Matrix factorization

CITATION

Yehuda Koren, Robert Bell, Chris Volinsky, "Matrix Factorization Techniques for Recommender Systems",

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