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Matrix Factorization Techniques for Recommender Systems

Yehuda KorenYahoo Labs, Sunnyvale, CA, USRobert BellAT and T Research Laboratories, USAChris VolinskyAT and T Research Laboratories, USA
2009en
ABI

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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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