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Issue No. 08 - August (2009 vol. 42)
ISSN: 0018-9162
pp: 30-37
Yehuda Koren , Yahoo Research
Robert Bell , AT&T Labs
Chris Volinsky , AT&T Labs
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", Computer, vol. 42, no. , pp. 30-37, August 2009, doi:10.1109/MC.2009.263
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