Issue No. 08 - August (2009 vol. 42)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MC.2009.263
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.
Computational intelligence, Netflix Prize, Matrix factorization
R. Bell, Y. Koren and C. Volinsky, "Matrix Factorization Techniques for Recommender Systems," in Computer, vol. 42, no. , pp. 30-37, 2009.