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Matrix Factorization Techniques for Recommender Systems
August 2009 (vol. 42 no. 8)
pp. 30-37
Yehuda Koren, Yahoo Research
Robert Bell, AT&T Labs
Chris Volinsky, AT&T Labs
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.

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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. 8, pp. 30-37, Aug. 2009, doi:10.1109/MC.2009.263
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