Y. Abbasi-yadkori, D. Pál, and C. Szepesvári, Improved Algorithms for Linear Stochastic Bandits, Neural Information Processing Systems, 2011.

J. D. Abernethy, E. Hazan, and A. Rakhlin, Competing in the Dark: An Efficient Algorithm for Bandit Linear Optimization, Conference on Learning Theory, 2008.

N. Alon, N. Cesa-bianchi, C. Gentile, and Y. Mansour, From Bandits to Experts: A Tale of Domination and Independence, NIPS, 2013.

P. Auer, Using confidence bounds for exploitationexploration trade-offs, Journal of Machine Learning Research, vol.3, pp.397-422, 2002.

P. Auer, R. Ortner, and . Revisited, Improved Regret Bounds for the Stochastic Multi-Armed Bandit Problem, Periodica Mathematica Hungarica, 2010.

K. Azuma, Weighted sums of certain dependent random variables, Tohoku Mathematical Journal, vol.19, issue.3, pp.357-367, 1967.
DOI : 10.2748/tmj/1178243286

A. Barabási, A. , and R. , Emergence of scaling in random networks, Science, vol.286, issue.11, 1999.

M. Belkin, I. Matveeva, and P. Niyogi, Regularization and Semi-Supervised Learning on Large Graphs, Conference on Learning Theory, 2004.

M. Belkin, P. Niyogi, and V. Sindhwani, Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples, Journal of Machine Learning Research, vol.7, pp.2399-2434, 2006.

D. Billsus, M. J. Pazzani, C. , and J. , A learning agent for wireless news access, Proceedings of the 5th international conference on Intelligent user interfaces , IUI '00, pp.33-36, 2000.
DOI : 10.1145/325737.325768

S. Bubeck, R. Munos, G. Stoltz, and C. Szepesvari, Xarmed bandits, Journal of Machine Learning Research, vol.12, pp.1587-1627, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00450235

S. Bubeck, N. Cesa-bianchi, and S. Kakade, Towards minimax policies for online linear optimization with bandit feedback, COLT, 2012.

S. Caron, B. Kveton, M. Lelarge, and S. Bhagat, Leveraging Side Observations in Stochastic Bandits, Uncertainty in Artificial Intelligence, pp.142-151, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01270324

N. Cesa-bianchi, C. Gentile, and G. Zappella, A Gang of Bandits, NIPS, 2013.

D. H. Chau, A. Kittur, J. I. Hong, F. , and C. , Apolo, Proceedings of the 2011 annual conference on Human factors in computing systems, CHI '11, 2011.
DOI : 10.1145/1978942.1978967

L. Chu, L. Li, L. Reyzin, and R. Schapire, Contextual Bandits with Linear Payoff Functions, AISTATS, 2011.

V. Dani, T. P. Hayes, and S. M. Kakade, Stochastic Linear Optimization under Bandit Feedback, The 21st Annual Conference on Learning Theory, 2008.

T. Desautels, A. Krause, and J. Burdick, Parallelizing Exploration-Exploitation Tradeoffs with Gaussian Process Bandit Optimization, ICML, 2012.

M. Jamali and M. Ester, A matrix factorization technique with trust propagation for recommendation in social networks, Proceedings of the fourth ACM conference on Recommender systems, RecSys '10, 2010.
DOI : 10.1145/1864708.1864736

D. Jannach, M. Zanker, A. Felfernig, F. , and G. , Recommender Systems: An Introduction, 2010.
DOI : 10.1017/CBO9780511763113

R. Keshavan, S. Oh, and A. Montanari, Matrix Completion from a Few Entries, IEEE International Symposium on Information Theory, pp.324-328, 2009.

R. Kleinberg, A. Slivkins, and E. Upfal, Multi-armed bandit problems in metric spaces, 40th ACM symposium on Theory Of Computing, 2008.

I. Koutis, G. L. Miller, and D. Tolliver, Combinatorial preconditioners and multilevel solvers for problems in computer vision and image processing, Computer Vision and Image Understanding, vol.115, issue.12, pp.1638-1646, 2011.
DOI : 10.1016/j.cviu.2011.05.013

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

M. Mcpherson, L. Smith-lovin, and J. Cook, Birds of a Feather: Homophily in Social Networks, Annual Review of Sociology, vol.27, issue.1, pp.415-444, 2001.
DOI : 10.1146/annurev.soc.27.1.415

O. Shamir, A Variant of Azuma's Inequality for Martingales with Subgaussian Tails. CoRR, abs, 1110.

A. Slivkins, Contextual Bandits with Similarity Information, Proceedings of the 24th annual Conference On Learning Theory, pp.1-27, 2009.

N. Srinivas, A. Krause, S. Kakade, and M. Seeger, Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design, Proceedings of International Conference on Machine Learning, 2010.

M. Valko, N. Korda, R. Munos, I. Flaounas, C. et al., Finite-Time Analysis of Kernelised Contextual Bandits, Uncertainty in Artificial Intelligence, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00826946

F. Zhang, The Schur complement and its applications, 2005.
DOI : 10.1007/b105056

X. Zhu, Semi-Supervised Learning Literature Survey, 2008.