Gábor Takács
Domonkos Tikk
ABSTRACT
Matrix Factorization (MF) based approaches have proven to be efficient for rating-based recommendation systems. In this work, we propose several matrix factorization approaches with improved prediction accuracy. We introduce a novel and fast (semi)-positive MF approach that approximates the features by using positive values for either users or items. We describe a momentum-based MF approach. A transductive version of MF is also introduced, which uses information from test instances (namely the ratings users have given for certain items) to improve prediction accuracy. We describe an incremental variant of MF that efficiently handles new users/ratings, which is crucial in a real-life recommender system. A hybrid MF--neighbor-based method is also discussed that further improves the performance of MF. The proposed methods are evaluated on the Netflix Prize dataset, and we show that they can achieve very favorable Quiz RMSE (best single method: 0.8904, combination: 0.8841) and running time.
- R. Bell, Y. Koren, and C. Volinsky. Chasing $1,000,000: How we won the netflix progress prize. ASA Statistical and Computing Graphics Newsletter, 18(2):4--12, December 2007.Google Scholar
- R. M. Bell and Y. Koren. Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights. In Proc of. ICDM, IEEE International Conference on Data Mining, 2007. Google ScholarDigital Library
- R. M. Bell, Y. Koren, and C. Volinsky. The BellKor solution to the Netflix Prize. Technical report, AT&T Labs Research, 2007. http://www.netflixprize.com/assets/ProgressPrize2007_KorBell.pdf.Google Scholar
- J. Bennett, C. Eklan, B. Liu, P. Smyth, and D. Tikk. KDD Cup and Workshop 2007. ACM SIGKDD Explorations Newsletter, 9(2):51--52, 2007. Google ScholarDigital Library
- J. Bennett and S. Lanning. The Netflix Prize. In Proc. of KDD Cup Workshop at SIGKDD'07, 13th ACM Int. Conf. on Knowledge Discovery and Data Mining, pages 3--6, San Jose, CA, USA, 2007.Google Scholar
- J. S. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proc. of UAI'98, 14th Conference on Uncertainty in Artificial Intelligence, pages 43--52. Morgan-Kaufmann, 1998. Google ScholarDigital Library
- D. Goldberg, D. Nichols, B. M. Oki, and D. Terry. Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12):61--70, 1992. Google ScholarDigital Library
- T. Hofmann. Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst., 22(1):89--115, 2004. Google ScholarDigital Library
- A. Paterek. Improving regularized singular value decomposition for collaborative filtering. In Proc. of KDD Cup Workshop at SIGKDD'07, 13th ACM Int. Conf. on Knowledge Discovery and Data Mining, pages 39--42, San Jose, CA, USA, 2007.Google Scholar
- P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. GroupLens: An open architecture for collaborative filtering of netnews. In Proc. of CSCW'94, ACM Conference on Computer Supported Cooperative Work, pages 175--186, Chapel Hill, North Carolina, United States, 1994. ACM Press. Google ScholarDigital Library
- R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In J. C. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems 20, Cambridge, MA, 2008. MIT Press.Google Scholar
- R. Salakhutdinov, A. Mnih, and G. Hinton. Restricted Boltzmann machines for collaborative filtering. In Proc. of ICML'07, the 24th Int. Conf. on Machine Learning, pages 791--798, Corvallis, OR, USA, 2007. Google ScholarDigital Library
- B. M. Sarwar, G. Karypis, J. A. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In Proc. of WWW'01: 10th Int. Conf. on World Wide Web, pages 285--295, Hong Kong, 2001. ACM Press. Google ScholarDigital Library
- N. Srebro, J. D. M. Rennie, and T. S. Jaakkola. Maximum-margin matrix factorization. Advances in Neural Information Processing Systems, 17, 2005.Google Scholar
- G. Takács, I. Pilászy, B. Németh, and D. Tikk. On the Gravity recommendation system. In Proc. of KDD Cup Workshop at SIGKDD'07, 13th ACM Int. Conf. on Knowledge Discovery and Data Mining, pages 22--30, San Jose, CA, USA, 2007.Google Scholar
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- Investigation of various matrix factorization methods for large recommender systems
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