S. Agrawal and N. Goyal, Analysis of thompson sampling for the multi-armed bandit problem, COLT, 2012.

S. Agrawal and N. Goyal, Thompson sampling for contextual bandits with linear payoffs, 2012.

J. Audibert and S. Bubeck, Minimax policies for adversarial and stochastic bandits, COLT, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00834882

J. Audibert, R. Munos, and C. Szepesvari, Tuning Bandit Algorithms in Stochastic Environments, ALT, 2007.
DOI : 10.1093/biomet/25.3-4.285

URL : https://hal.archives-ouvertes.fr/inria-00203487

P. Auer, N. Cesa-bianchi, and P. Fischer, Finite-time analysis of the multiarmed bandit problem, Mach. Learn, 2002.

D. M. Blei, A. Y. Ng, and M. I. Jordan, Latent dirichlet allocation, J. Mach. Learn. Res, 2003.

S. Buccapatnam, A. Eryilmaz, and N. B. Shroff, Stochastic bandits with side observations on networks, 2014.

O. Chapelle, L. , and L. , An empirical evaluation of thompson sampling, NIPS, 2011.

W. Chen, Y. Wang, and Y. Yuan, Combinatorial multiarmed bandit: General framework and applications, ICML, 2013.

W. Chu, L. Li, L. Reyzin, R. E. Schapire, . Aistats et al., Contextual bandits with linear payoff functions Emerging topic detection for business intelligence via predictive analysis of 'meme' dynamics Towards scaling fully personalized pageRank: algorithms, lower bounds, and experiments, AAAI Spring Symposium, 2005.

T. Gisselbrecht, L. Denoyer, P. Gallinari, and S. Lamprier, Whichstreams: A dynamic approach for focused data capture from large social media, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01355397

P. Gupta, A. Goel, J. Lin, A. Sharma, D. Wang et al., WTF, Proceedings of the 22nd international conference on World Wide Web, WWW '13, 2010.
DOI : 10.1145/2488388.2488433

L. Hong and B. D. Davison, Empirical study of topic modeling in Twitter, Proceedings of the First Workshop on Social Media Analytics, SOMA '10, 2010.
DOI : 10.1145/1964858.1964870

E. Kaufmann, O. Cappe, and A. R. Garivier, On bayesian upper confidence bounds for bandit problems Thompson sampling: An asymptotically optimal finite-time analysis, 2012.

P. Kohli, M. Salek, and G. Stoddard, A fast bandit algorithm for recommendation to users with heterogenous tastes, AAAI, 2013.

R. Lage, L. Denoyer, P. Gallinari, and P. Dolog, Choosing which message to publish on social networks, Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM '13, 2013.
DOI : 10.1145/2492517.2492541

URL : https://hal.archives-ouvertes.fr/hal-01215178

T. Lai and H. Robbins, Asymptotically efficient adaptive allocation rules, Advances in Applied Mathematics, vol.6, issue.1, pp.4-22, 1985.
DOI : 10.1016/0196-8858(85)90002-8

L. Li, W. Chu, J. Langford, and R. E. Schapire, A contextual-bandit approach to personalized news article recommendation, Proceedings of the 19th international conference on World wide web, WWW '10, 2010.
DOI : 10.1145/1772690.1772758

R. Li, S. Wang, C. , K. C. , and -. , Towards social data platform, Proc. VLDB Endow, 2013.
DOI : 10.14778/2556549.2556577

L. Qin, S. Chen, and X. Zhu, Contextual Combinatorial Bandit and its Application on Diversified Online Recommendation, SIAM, 2014.
DOI : 10.1137/1.9781611973440.53

J. Weng, E. Lim, J. Jiang, and Q. He, TwitterRank, Proceedings of the third ACM international conference on Web search and data mining, WSDM '10, 2010.
DOI : 10.1145/1718487.1718520