D. Agarwal, B. C. Chen, P. Elango, N. Motgi, S. T. Park et al., Online models for content optimization, Proc. of NIPS'08, pp.17-24, 2008.

J. Y. Audibert, R. Munos, and C. Szepesvári, Exploration???exploitation tradeoff using variance estimates in multi-armed bandits, Theoretical Computer Science, vol.410, issue.19, pp.1876-1902, 2009.
DOI : 10.1016/j.tcs.2009.01.016
URL : https://hal.archives-ouvertes.fr/hal-00711069

P. Auer, N. Cesa-bianchi, and P. Fischer, Finite-time analysis of the multiarmed bandit problem, Machine Learning, vol.47, issue.2/3, pp.235-256, 2002.
DOI : 10.1023/A:1013689704352

J. Bennett and S. Lanning, Netflix: The Netflix prize, In: KDD Cup and Workshop, 2007.

S. Bhagat, U. Weinsberg, S. Ioannidis, and N. Taft, Recommending with an agenda, Proceedings of the 8th ACM Conference on Recommender systems, RecSys '14, pp.65-72, 2014.
DOI : 10.1145/2645710.2645747

L. Bottou and O. Bousquet, The tradeoffs of large scale learning, Proc. of NIPS, pp.161-168, 2007.

O. Chapelle and L. Li, An empirical evaluation of thompson sampling, Proc. of NIPS'11, pp.2249-2257, 2011.

P. Cremonesi, Y. Koren, and R. Turrin, Performance of recommender algorithms on top-n recommendation tasks, Proceedings of the fourth ACM conference on Recommender systems, RecSys '10, pp.39-46, 2010.
DOI : 10.1145/1864708.1864721

F. Garcin, B. Faltings, O. Donatsch, A. Alazzawi, C. Bruttin et al., Offline and online evaluation of news recommender systems at swissinfo.ch, Proceedings of the 8th ACM Conference on Recommender systems, RecSys '14, pp.169-176, 2014.
DOI : 10.1145/2645710.2645745

A. Garivier and O. Cappé, The KL-UCB algorithm for bounded stochastic bandits and beyond, Proc. of COLT'11, pp.359-376, 2011.

F. M. Harper and J. A. Konstan, The MovieLens Datasets, ACM Transactions on Interactive Intelligent Systems, vol.5, issue.4, p.19, 2015.
DOI : 10.1145/2827872

J. Kawale, H. Bui, B. Kveton, L. T. Thanh, and S. Chawla, Efficient thompson sampling for online matrix-factorization recommendation, p.15, 2015.

Y. Koren, R. Bell, and C. Volinsky, Matrix Factorization Techniques for Recommender Systems, Computer, vol.42, issue.8, pp.30-37, 2009.
DOI : 10.1109/MC.2009.263

Y. Koren and J. Sill, OrdRec, Proceedings of the fifth ACM conference on Recommender systems, RecSys '11, pp.117-124, 2011.
DOI : 10.1145/2043932.2043956

J. Langford, A. Strehl, and J. Wortman, Exploration scavenging, Proceedings of the 25th international conference on Machine learning, ICML '08, pp.528-535, 2008.
DOI : 10.1145/1390156.1390223
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.5626

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, pp.661-670, 2010.
DOI : 10.1145/1772690.1772758
URL : http://arxiv.org/abs/1003.0146

L. Li, W. Chu, J. Langford, and X. Wang, Unbiased offline evaluation of contextualbandit-based news article recommendation algorithms, Proc. of WSDM'11, pp.297-306, 2011.
DOI : 10.1145/1935826.1935878

H. Ma, D. Zhou, C. Liu, M. R. Lyu, and I. King, Recommender systems with social regularization, Proceedings of the fourth ACM international conference on Web search and data mining, WSDM '11, pp.287-296, 2011.
DOI : 10.1145/1935826.1935877

J. Mary, R. Gaudel, and P. Preux, Bandits and Recommender Systems, Proc. of Mach. Learn., Optimization and big Data (MOD'15), 2015.
DOI : 10.1007/978-3-319-27926-8_29
URL : https://hal.archives-ouvertes.fr/hal-01256033

A. Nakamura, A ucb-like strategy of collaborative filtering, Proc. of ACML'14, 2014.

S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-thieme, BPR: Bayesian personalized ranking from implicit feedback, Proc. of UAI'09, pp.452-461, 2009.

A. Said and A. Bellogín, Comparative recommender system evaluation, Proceedings of the 8th ACM Conference on Recommender systems, RecSys '14, pp.129-136, 2014.
DOI : 10.1145/2645710.2645746

Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson, N. Oliver et al., CLiMF, Proceedings of the sixth ACM conference on Recommender systems, RecSys '12, pp.139-146, 2012.
DOI : 10.1145/2365952.2365981

L. Tang, Y. Jiang, L. Li, and T. Li, Ensemble contextual bandits for personalized recommendation, Proceedings of the 8th ACM Conference on Recommender systems, RecSys '14, p.14, 2014.
DOI : 10.1145/2645710.2645732
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.696.8725

J. Weston, H. Yee, and R. J. Weiss, Learning to rank recommendations with the korder statistic loss, Proc. of RecSys'13, pp.245-248, 2013.

Z. Xing, X. Wang, and Y. Wang, Enhancing Collaborative Filtering Music Recommendation by Balancing Exploration and Exploitation, Proc. of int. soc. for Music Inf. Retr. (ISMIR), pp.445-450, 2014.

X. Zhao, W. Zhang, and J. Wang, Interactive collaborative filtering, Proceedings of the 22nd ACM international conference on Conference on information & knowledge management, CIKM '13, pp.1411-1420, 2013.
DOI : 10.1145/2505515.2505690

Y. Zhou, D. Wilkinson, R. Schreiber, and R. Pan, Large-Scale Parallel Collaborative Filtering for the Netflix Prize, Proc. of Alg. Aspects in Information and Management (AAIM'08, pp.337-348, 2008.
DOI : 10.1007/978-3-540-68880-8_32
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.173.2797