Robert M. Bell
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Abstract
This article outlines the overall strategy and summarizes a few key innovations of the team that won the first Netflix progress prize.
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Digital Library
- R. M. Bell and Y. Koren, "Improved Neighborhood-based Collaborative Filtering", Proc. KDD-Cup and Workshop at the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2007.Google Scholar
- R. M. Bell and Y. Koren, "Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights", Proc. IEEE International Conference on Data Mining (ICDM'07), 2007. 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
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- Netflix, Inc., "Netflix Prize: Forum", www.netflixprize.com//community/.Google Scholar
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- R. Salakhutdinov, A. Mnih and G. Hinton, "Restricted Boltzmann Machines for Collaborative Filtering", Proc. 24th Annual International Conference on Machine Learning (ICML'07), 2007. Google ScholarDigital Library
- B. Sarwar, G. Karypis, J. Konstan and J. Riedl, "Itembased Collaborative Filtering Recommendation Algorithms", Proc. 10th International Conference on the World Wide Web, pp. 285--295, 2001. Google ScholarDigital Library
- Y. Zhang and J. Koren, "Efficient Bayesian Hierarchical User Modeling for Recommendation Systems", Proc. 30th International ACM Conference on Research and Development in Information Retrieval (SIGIR'07), 2007. Google ScholarDigital Library
Index Terms
- Lessons from the Netflix prize challenge
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