Issue No. 08 - August (2009 vol. 42)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MC.2009.263
Robert Bell , AT&T Labs
Yehuda Koren , Yahoo Research
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
Robert Bell, Yehuda Koren, Chris Volinsky, "Matrix Factorization Techniques for Recommender Systems", Computer, vol. 42, no. , pp. 30-37, August 2009, doi:10.1109/MC.2009.263