Efficient retrieval of recommendations in a matrix factorization framework

ACM International Conference Proceeding Series

Volumn , Issue , 2012, Pages 535-544

  Koenigstein, Noam ^a   Ram, Parikshit ^b   Shavitt, Yuval ^a  

^a TEL AVIV UNIVERSITY   (Israel)

^b Georgia Institute of Technology   (United States)

Author keywords

collaborative filtering; fast retrieval; inner product

Indexed keywords

APPROXIMATE RESULTS; APPROXIMATE SOLUTION; COLLABORATIVE FILTERING; DATA SETS; EXACT SOLUTION; FAST RETRIEVAL; INNER PRODUCT; LINEAR SEARCH; LOW-RANK MATRICES; MATRIX FACTORIZATIONS; METRIC TREES; RETRIEVAL EFFICIENCY; THEORETICAL ERRORS;

ALGORITHMS; BINARY TREES; DATA PROCESSING; FILTRATION; KNOWLEDGE MANAGEMENT;

QUALITY CONTROL;

EID: 84871067909     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2396761.2396831     Document Type: Conference Paper

References (19)

1. R. M. Bell and Y. Koren. Lessons from the netflix prize challenge. SIGKDD Explor. Newsl., 2007.
2. J. Bennett and S. Lanning. The netflix prize. In Proc. KDD Cup and Workshop, 2007.
3. M. S. Charikar. Similarity estimation techniques from rounding algorithms. In Proceedings of the thiry-fourth annual ACM symposium on Theory of computing, 2002.
4. K. Clarkson. Nearest-neighbor searching and metric space dimensions. Nearest-Neighbor Methods for Learning and Vision: Theory and Practice, 2006.
5. A. S. Das, M. Datar, A. Garg, and S. Rajaram. Google news personalization: scalable online collaborative filtering. In Proceedings of the 16th international conference on World Wide Web, 2007.
6. G. Dror, N. Koenigstein, and Y. Koren. Yahoo! music recommendations: Modeling music ratings with temporal dynamics and item taxonomy. In Proc. 5th ACM Conference on Recommender Systems, 2011.
7. G. Dror, N. Koenigstein, and Y. Koren. Web scale media recommendation systems. Proceedings of the IEEE, pages 1-15, 2012.
8. G. Dror, N. Koenigstein, Y. Koren, and M. Weimer. The yahoo! music dataset and kdd-cup'11. Journal Of Machine Learning Research, 17:1-12, 2011.
9. P. Indyk and R. Motwani. Approximate Nearest Neighbors: Towards Removing the Curse of Dimensionality. In STOC, 1998.
10. G. Karypis. CLUTO a clustering toolkit. Technical report, Dept. of Computer Science, University of Minnesota, 2002.
11. M. Khoshneshin and W. N. Street. Collaborative filtering via euclidean embedding. In Proceedings of the fourth ACM conference on Recommender systems, 2010.
12. N. Koenigstein, N. Nice, U. Paquet, and N. Schleyen. The xbox recommender system. In Proc. 6th ACM Conference on Recommender Systems, 2012.
13. Y. Koren, R. M. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. IEEE Computer, 2009.
14. T. Liu, A. W. Moore, A. G. Gray, and K. Yang. An Investigation of Practical Approximate Nearest Neighbor Algorithms. In Advances in Neural Information Processing Systems 17, 2005.
15. S. M. Omohundro. Five Balltree Construction Algorithms. Technical report, International Computer Science Institute, December 1989.
16. F. P. Preparata and M. I. Shamos. Computational Geometry: An Introduction. Springer, 1985.
17. P. Ram and A. Gray. Maximum inner-product search using cone trees. In SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2012.
18. P. Ram, D. Lee, H. Ouyang, and A. G. Gray. Rank-approximate nearest neighbor search: Retaining meaning and speed in high dimensions. In Advances in Neural Information Processing Systems 22. 2009.
19. S. Zhang, W. Wang, J. Ford, and F. Makedon. Learning from incomplete ratings using non-negative matrix factorization. In Proceedings of the Sixth SIAM International Conference on Data Mining, 2006.