Linear bandits in high dimension and recommendation systems

2012 50th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2012

Volumn , Issue , 2012, Pages 1750-1754

  Deshpande, Yash ^a   Montanari, Andrea ^b  

^a Stanford University   (United States)

^b STANFORD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACHIEVABILITY; DEMOGRAPHIC PROFILE; HIGH DIMENSIONS; HIGH-DIMENSIONAL; MULTI ARMED BANDIT; ON-LINE SERVICE; UPPER AND LOWER BOUNDS; USER'S PREFERENCES;

COMMUNICATION;

STATISTICAL TESTS;

EID: 84875747480     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/Allerton.2012.6483433     Document Type: Conference Paper

References (13)

1. A. Schein, A. Popescul, L. Ungar, and D. Pennock, "Methods and metrics for cold-start recommendations," in Proceedings of the 25th annual international ACM SIGIR Conference on Research and Development in Information Retrieval, 2002, pp. 253-260.
2. M. Zhang and N. Hurley, "Avoiding monotony: improving the diversity of recommendation lists," in Proceedings of the 2008 ACM conference on Recommender Systems, 2008.
3. M. Slaney and W. White, "Measuring playlist diversity for recommendation systems," in Proceedings of the 1st ACM workshop on Audio and music computing multimedia, 2006.
4. N. Srebro and T. Jaakkola, "Weighted low-rank approximations," in 20th International Conference on Machine Learning. AAAI Press, 2003, pp. 720-727.
5. N. Srebro, J. D. M. Rennie, and T. S. Jaakola, "Maximum-margin matrix factorization," in Advances in Neural Information Processing Systems 17. MIT Press, 2005, pp. 1329-1336.
6. E. J. Candès and B. Recht, "Exact matrix completion via convex optimization," Foundation of computational mathematics, vol. 9, no. 6, pp. 717-772, February 2009.
7. D. Gross, "Recovering low-rank matrices from few coefficients in any basis," 2009, arXiv:0910.1879.
8. R. H. Keshavan, A. Montanari, and S. Oh, "Matrix completion from noisy entries," J. Mach. Learn. Res., vol. 11, pp. 2057-2078, July 2010.
9. V. Koltchinskii, K. Lounici, and A. Tsybakov, "Nuclear-norm penalization and optimal rates for noisy low-rank matrix completion," Ann. Statist., vol. 39, pp. 2302-2329, 2011.
10. V. Dani, T. Hayes, and S. Kakade, "Stochastic linear optimization under bandit feedback," in COLT, 2008, pp. 355-366.
11. P. Rusmevichientong and J. Tsitsiklis, "Linearly parameterized bandits," Math. Oper. Res., vol. 35, no. 2, pp. 395-411, 2010.
12. Y. Abbasi-Yadkori, D. Pál, and C. Szepesvári, "Improved algorithms for linear stochastic bandits," in NIPS, 2011, pp. 2312-2320.
13. P. Auer, "Using confidence bounds for exploitation-exploration trade-offs," Journal of Machine Learning Research, vol. 3, p. 2002, 2002.