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Matrix Factorization Techniques for Recommender Systems

by: Yehuda Koren, Robert Bell, Chris Volinsky
Computer In Computer, Vol. 42, No. 8. (07 August 2009), pp. 30-37, doi:10.1109/mc.2009.263  Key: citeulike:5392739

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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.


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