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Issue No.08 - August (2009 vol.42)
pp: 30-37
Yehuda Koren , Yahoo Research
Chris Volinsky , AT&T Labs
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.
INDEX TERMS
Computational intelligence, Netflix Prize, Matrix factorization
CITATION
Yehuda Koren, Chris Volinsky, "Matrix Factorization Techniques for Recommender Systems", Computer, vol.42, no. 8, pp. 30-37, August 2009, doi:10.1109/MC.2009.263
REFERENCES
1. D. Goldberg et al., "Using Col-laborative Filtering to Weave an Information Tapestry," Comm. ACM, vol. 35, 1992, pp. 61-70.
2. B.M. Sarwar et al., "Application of Dimensionality Reduction in Recommender System—A Case Study," Proc. KDD Workshop on Web Mining for e-Commerce: Challenges and Opportunities (WebKDD), ACM Press, 2000.
3. S. Funk, "Netflix Update: Try This at Home," Dec. 2006; http://sifter.org/~simon/journal20061211.html .
4. Y. Koren, "Factorization Meets the Neighborhood: A Multifaceted Collaborative Filtering Model," Proc. 14th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, ACM Press, 2008, pp. 426-434.
5. A. Paterek, "Improving Regularized Singular Value Decomposition for Collaborative Filtering," Proc. KDD Cup and Workshop, ACM Press, 2007, pp. 39-42.
6. G. Takàcs et al., "Major Components of the Gravity Recommendation System," SIGKDD Explorations, vol. 9, 2007, pp. 80-84.
7. R. Salakhutdinov and A. Mnih, "Probabilistic Matrix Factorization," Proc. Advances in Neural Information Processing Systems 20 (NIPS 07), ACM Press, 2008, pp. 1257-1264.
8. R. Bell and Y. Koren, "Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights," Proc. IEEE Int'l Conf. Data Mining (ICDM 07), IEEE CS Press, 2007, pp. 43-52.
9. Y. Zhou et al., "Large-Scale Parallel Collaborative Filtering for the Netflix Prize," Proc. 4th Int'l Conf. Algorithmic Aspects in Information and Management, LNCS 5034, Springer, 2008, pp. 337-348.
10. Y.F. Hu, Y. Koren, and C. Volinsky, "Collaborative Filtering for Implicit Feedback Datasets," Proc. IEEE Int'l Conf. Data Mining (ICDM 08), IEEE CS Press, 2008, pp. 263-272.
11. Y. Koren, "Collaborative Filtering with Temporal Dynamics," Proc. 15th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD 09), ACM Press, 2009, pp. 447-455.
12. J. Bennet and S. Lanning, "The Netflix Prize," KDD Cup and Workshop, 2007; www.netflixprize.com.
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