Yehuda Koren
ABSTRACT
Recommender systems provide users with personalized suggestions for products or services. These systems often rely on Collaborating Filtering (CF), where past transactions are analyzed in order to establish connections between users and products. The two more successful approaches to CF are latent factor models, which directly profile both users and products, and neighborhood models, which analyze similarities between products or users. In this work we introduce some innovations to both approaches. The factor and neighborhood models can now be smoothly merged, thereby building a more accurate combined model. Further accuracy improvements are achieved by extending the models to exploit both explicit and implicit feedback by the users. The methods are tested on the Netflix data. Results are better than those previously published on that dataset. In addition, we suggest a new evaluation metric, which highlights the differences among methods, based on their performance at a top-K recommendation task.
- . G. Adomavicius and A. Tuzhilin, "Towards the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions", IEEE Transactions on Knowledge and Data Engineering 17 (2005), 634--749. Google Scholar
Digital Library
- . G. Adomavicius and A. Tuzhilin, "Towards the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions", IEEE Transactions on Knowledge and Data Engineering 17 (2005), 634--749. Google ScholarDigital Library
- . R. Bell and Y. Koren, "Lessons from the Netflix Prize Challenge", SIGKDD Explorations 9 (2007), 75--79. Google ScholarDigital Library
- . R. M. Bell, Y. Koren and C. Volinsky, "Modeling Relationships at Multiple Scales to Improve Accuracy of Large Recommender Systems", Proc. 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2007. Google ScholarDigital Library
- . J. Bennet and S. Lanning, "The Netflix Prize", KDD Cup and Workshop, 2007. www.netflixprize.com.Google Scholar
- . J. Canny, "Collaborative Filtering with Privacy via Factor Analysis", Proc. 25th ACM SIGIR Conf.on Research and Development in Information Retrieval (SIGIR'02), pp. 238--245, 2002. Google ScholarDigital Library
- D. Blei, A. Ng, and M. Jordan, "Latent Dirichlet Allocation", Journal of Machine Learning Research 3 (2003), 993--1022. Google ScholarDigital Library
- S. Deerwester, S. Dumais, G. W. Furnas, T. K. Landauer and R. Harshman, "Indexing by Latent Semantic Analysis", Journal of the Society for Information Science 41 (1990), 391--407.Google ScholarCross Ref
- S. Funk, "Netflix Update: Try This At Home", http://sifter.org/?simon/journal/20061211.html, 2006.Google Scholar
- D. Goldberg, D. Nichols, B. M. Oki and D. Terry, "Using Collaborative Filtering to Weave an Information Tapestry", Communications of the ACM 35 (1992), 61--70. Google ScholarDigital Library
- J. L. Herlocker, J. A. Konstan and J. Riedl, "Explaining Collaborative Filtering Recommendations", Proc. ACM conference on Computer Supported Cooperative Work, pp. 241--250, 2000. Google ScholarDigital Library
- J. L. Herlocker, J. A. Konstan, A. Borchers and John Riedl, "An Algorithmic Framework for Performing Collaborative Filtering", Proc. 22nd ACM SIGIR Conference on Information Retrieval, pp. 230--237, 1999. Google ScholarDigital Library
- T. Hofmann, "Latent Semantic Models for Collaborative Filtering", ACM Transactions on Information Systems 22 (2004), 89--115. Google ScholarDigital Library
- D. Kim and B. Yum, "Collaborative Filtering Based on Iterative Principal Component Analysis", Expert Systems with Applications 28 (2005), 823--830. Google ScholarDigital Library
- G. Linden, B. Smith and J. York, "Amazon.com Recommendations: Item-to-item Collaborative Filtering", IEEE Internet Computing 7 (2003), 76--80. Google ScholarDigital Library
- D. W. Oard and J. Kim, "Implicit Feedback for Recommender Systems", Proc. 5th DELOS Workshop on Filtering and Collaborative Filtering, pp. 31--36, 1998.Google Scholar
- A. Paterek, "Improving Regularized Singular Value Decomposition for Collaborative Filtering", Proc. KDD Cup and Workshop, 2007.Google Scholar
- R. Salakhutdinov, A. Mnih and G. Hinton, "Restricted Boltzmann Machines for Collaborative Filtering", Proc. 24th Annual International Conference on Machine Learning, pp. 791--798, 2007. Google ScholarDigital Library
- R. Salakhutdinov and A. Mnih, "Probabilistic Matrix Factorization", Advances in Neural Information Processing Systems 20 (NIPS'07), pp. 1257--1264, 2008.Google ScholarDigital Library
- B. M. Sarwar, G. Karypis, J. A. Konstan, and J. Riedl, "Application of Dimensionality Reduction in Recommender System -- A Case Study", WEBKDD'2000.Google Scholar
- B. Sarwar, G. Karypis, J. Konstan and J. Riedl, "Item-based Collaborative Filtering Recommendation Algorithms", Proc. 10th International Conference on the World Wide Web, pp. 285--295, 2001. Google ScholarDigital Library
- G. Takacs, I. Pilaszy, B. Nemeth and D. Tikk, "Major Components of the Gravity Recommendation System", SIGKDD Explorations 9 (2007), 80--84. Google ScholarDigital Library
- N. Tintarev and J. Masthoff, "A Survey of Explanations in Recommender Systems", ICDE'07 Workshop on Recommender Systems and Intelligent User Interfaces, 2007. Google ScholarDigital Library
Index Terms
- Factorization meets the neighborhood: a multifaceted collaborative filtering model
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