Collaborative filtering with low regret

SIGMETRICS/ Performance 2016 - Proceedings of the SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Science

Volumn , Issue , 2016, Pages 207-220

  Bresler, Guy ^a   Shah, Devavrat ^a   Voloch, Luis F ^a  

^a IDSS LIDS EECS   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS;

BINARY MATRIX; COLD START; COLLABORATIVE FILTERING ALGORITHMS; RANDOM USERS; RECOMMENDATION ALGORITHMS;

COLLABORATIVE FILTERING;

EID: 84978633421     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2896377.2901469     Document Type: Conference Paper

References (37)

1. N. Alon, N. Cesa-Bianchi, C. Gentile, S. Mannor, Y. Mansour, and O. Shamir. Non stochastic multi-armed bandits with graph-structured feedback. arXiv Technical Report arXiv:1409.8428, 2014.
2. S. Arora, R. Ge, R. Kannan, and A. Moitra. Computing a nonnegative matrix factorization-provably. In STOC, 2012.
3. A. Bellogin and J. Parapar. Using graph partitioning techniques for neighbour selection in user-based collaborative filtering. In Rec Sys, 2012.
4. G. Biau, B. Cadre, and L. Rouviere. Statistical analysis of k-nearest neighbor collaborative recommendation. The Annals of Statistics, 38(3):1568-1592, 2010.
5. G. Bresler, G. Chen, and D. Shah. A latent source model for online collaborative filtering. In NIPS, 2014.
6. S. Bubeck and N. Cesa-Bianchi. Regret analysis of stochastic and non stochastic multi-armed bandit problems. arXiv preprint arXiv:1204.5721, 2012.
7. S. Bubeck, R. Munos, G. Stoltz, and C. Szepesvari. X-armed bandits. JMLR, 2011.
8. S. Caron and S. Bhagat. Mixing bandits: A recipe for improved cold-start recommendations in a social network. In Workshop on Social Network Mining and Analysis, 2013.
9. N. Cesa-Bianchi, C. Gentile, and G. Zappella. A gang of bandits. In NIPS, 2013.
10. N. Cesa-Bianchi and O. Shamir. Efficient trans ductive online learning via randomized rounding. In Empirical Inference, pages 177-194. Springer, 2013.
11. O. Dabeer. Adaptive collaborating filtering: The low noise regime. In ISIT, 2013.
12. A. S. Das, M. Datar, A. Garg, and S. Rajaram. Google news personalization: scalable online collaborative filtering. In WWW, 2007.
13. S. Dasgupta and K. Sinha. Randomized partition trees for nearest neighbor search. Algorithmica, 2014.
14. D. Donoho and V. Stodden. When does non-negative matrix factorization give a correct decomposition into parts? In NIPS, 2003.
15. C. Gentile, S. Li, and G. Zappella. Online clustering of bandits. ICML, 2014.
16. D. Goldberg, D. Nichols, B. M. Oki, and D. Terry. Using collaborative filtering to weave an information tapestry. Commun. ACM, 1992.
17. K. Goldberg, T. Roeder, D. Gupta, and C. Perkins. Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval, 2001.
18. S. Har-Peled and M. Mendel. Fast construction of nets in low-dimensional metrics and their applications. SIAM Journal on Computing, 35(5):1148-1184, 2006.
19. E. Hazan, S. Kale, and S. Shalev-Shwartz. Near-optimal algorithms for online matrix prediction. COLT, 2012.
20. J. Heinonen. Lectures on analysis on metric spaces. Springer, 2001.
21. J. Kleinberg and M. Sandler. Using mixture models for collaborative filtering. In STOC, 2004.
22. R. Kleinberg, A. Slivkins, and E. Upfal. Bandits and experts in metric spaces. arXiv preprint arXiv:1312.1277, 2013.
23. Y. Koren and R. Bell. Advances in collaborative filtering. In Recommender Systems Handbook, pages 145-186. Springer US, 2011.
24. Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. IEEE, 2009.
25. D. D. Lee and H. S. Seung. Algorithms for non-negative matrix factorization. In NIPS, 2001.
26. G. Linden, B. Smith, and J. York. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7(1):76-80, 2003.
27. L. Massoulie, M. Ohannessian, and A. Proutiere. Greedy-bayes approach for targeted news dissemination. Sigmetrics, 2015.
28. C. McDiarmid. Concentration. In Probabilistic methods for algorithmic discrete mathematics. Springer, 1998.
29. P. Melville, R. J. Mooney, and R. Nagarajan. Content-boosted collaborative filtering for improved recommendations. In AAAI/IAAI, 2002.
30. A. Moitra. Algorithmic aspects of machine learning. Online Manuscript, 2014.
31. J. Riedl and J. Konstan. Movielens dataset, 1998.
32. A. Slivkins. Contextual bandits with similarity information. The Journal of Machine Learning Research, 2014.
33. W. R. Thompson. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika, pages 285-294, 1933.
34. S. A. Vavasis. On the complexity of nonnegative matrix factorization. SIAM Journal on Optimization, 2009.
35. K. Verstrepen and B. Goethals. Unifying nearest neighbors collaborative filtering. In Recsys, 2014.
36. J. Wang, A. De Vries, and M. Reinders. Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In ACM SIGIR, 2006.
37. T. Zhang. Solving large scale linear prediction problems using stochastic gradient descent algorithms. In ICML, 2004.