Matrix Factorization Techniques for Recommender Systems

Yehuda Koren; Robert Bell; Chris Volinsky · Computer · 2009

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Yehuda Koren; Robert Bell; Chris Volinsky

Publisher

Computer

Year

2009

Language

English

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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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APA 7

Koren, Y, Bell, R, & Volinsky, C. (2009). Matrix Factorization Techniques for Recommender Systems. https://doi.org/10.1109/mc.2009.263

MLA

Koren, Yehuda, et al. "Matrix Factorization Techniques for Recommender Systems." 2009. https://doi.org/10.1109/mc.2009.263.

Chicago

Koren, Yehuda, Robert Bell, and Chris Volinsky. 2009. "Matrix Factorization Techniques for Recommender Systems.". https://doi.org/10.1109/mc.2009.263.

Harvard

Koren, Y, Bell, R. and Volinsky, C. 2009, Matrix Factorization Techniques for Recommender Systems, Computer, available at: https://doi.org/10.1109/mc.2009.263 [Accessed 7 Aug. 2026].

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Title: Matrix Factorization Techniques for Recommender Systems

Author / contributors: Yehuda Koren; Robert Bell; Chris Volinsky

Publisher: Computer

Publication year: 2009

Language: English

Subjects

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Recommender system; Computer science; Matrix decomposition; Factorization; Product (mathematics); Matrix (chemical analysis); Non-negative matrix factorization; Artificial intelligence; Information retrieval; Theoretical computer science; Algorithm; Mathematics