D. Krass and K. Ross, Applying Machine Learning Techniques to Improve User Acceptance on Ubiquitous Environment Bouneffouf Djallel, Situation-Aware Approach to Improve Context-based Recommender System, arXiv preprint arXiv:1303.0481, 2013 [18]: Bouneffouf Djallel, Improving adaptation of ubiquitous recommander systems by using reinforcement learning and collaborative filtering,arXiv preprint arXiv:1303.2308, 2013 [16]: Bouneffouf Djallel, Evolution of the user's content: An Overview of the state of the art, arXiv preprint arXiv:1305.1787, 2013 [17]: Bouneffouf Djallel, The Impact of Situation Clustering in Contextual-Bandit Algorithm for Context-Aware Recommender Systems, arXiv preprint arXiv:1304 Bouneffouf Djallel, L'apprentissage automatique, uné etape importante dans l'adaptation des systèmes d'informationàinformation`informationà l'utilisateur, Inforsid Percentile performance criteria for limiting average Markov decision processes, IMMoA12 : 2nd International Workshop on Information Management for Mobile Applications conjunction with VLDB '12 : 38th International Conference on Very Large Databases Bouneffouf Djallel, Etude des dimensions spéecifiques du contexte dans un syst\eme de filtrage d'informations, arXiv preprint arXiv:1405.6287 Bouneffouf Djallel, Towards User Profile Modelling in Recommender System, arXiv preprint arXiv:1305.1114 Bouneffouf Djallel, Mobile Recommender Systems Methods: An Overview Advanced Courses, pp.26-32, 1995.

G. D. Abowd, A. K. Dey, P. J. Brown, N. Davies, M. Smith et al., Towards a better understanding of context and contextawareness, Proceedings of the 1st international symposium on Handheld and Ubiquitous Computing, HUC '99, pp.304-307, 1999.

G. Adomavicius and A. Tuzhilin, Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions, IEEE Transactions on Knowledge and Data Engineering, vol.17, issue.6, pp.734-749, 2005.
DOI : 10.1109/TKDE.2005.99

C. R. Anderson, A machine learning approach to web personalization, 2002.

A. Jajvand and A. S. Mir-ali-seyyedi, A Hybrid Recommender System for Service Discovery, International Journal of Innovative Research in Computer and Communication Engineering, vol.4, issue.2, pp.1344-1347, 2013.

P. Auer, N. Cesa-bianchi, Y. Freund, and R. E. Schapire, Gambling in a rigged casino: The adversarial multi-armed bandit problem, Proceedings of IEEE 36th Annual Foundations of Computer Science, p.322, 1995.
DOI : 10.1109/SFCS.1995.492488

P. Auer, N. Cesa-bianchi, and P. Fischer, Finite-time analysis of the multiarmed bandit problem, Machine Learning, pp.235-256, 2002.

R. A. Baeza-yates and B. Ribeiro-neto, Modern Information Retrieval, 1999.

M. Balabanovic and Y. Shoham, Fab: content-based, collaborative recommendation, Communications of the ACM, vol.40, issue.3, pp.66-72, 1997.
DOI : 10.1145/245108.245124

C. Bettini, O. Brdiczka, K. Henricksen, J. Indulska, D. Nicklas et al., A survey of context modelling and reasoning techniques, ¡ce:title¿Context Modelling, Reasoning and Management¡/ce:title¿, pp.161-180, 2010.
DOI : 10.1016/j.pmcj.2009.06.002

N. Bila, J. Cao, R. Dinoff, T. K. Ho, R. Hull et al., Mobile User Profile Acquisition through Network Observables and Explicit User Queries, The Ninth International Conference on Mobile Data Management (mdm 2008), pp.98-107, 2008.
DOI : 10.1109/MDM.2008.34

N. Bila, J. Cao, R. Dinoff, T. K. Ho, B. Kumar et al., Intuitive network applications: Learning user context and behavior, Bell Labs Technical Journal, vol.13, issue.2, pp.31-47, 2008.
DOI : 10.1002/bltj.20301

T. Bogers and A. Van-den-bosch, Collaborative and Content-based Filtering for Item Recommendation on Social Bookmarking Websites, 2009.

O. Bouidghaghen, L. Tamine, and M. Boughanem, Context-Aware User's Interests for Personalizing Mobile Search, 2011 IEEE 12th International Conference on Mobile Data Management, pp.129-134, 2011.
DOI : 10.1109/MDM.2011.51

D. Bouneffouf, Applying machine learning techniques to improve user acceptance on ubiquitous environement, 2013.

D. Bouneffouf, Evolution of the user's content: An overview of the state of the art, 2013.

D. Bouneffouf, The impact of situation clustering in contextualbandit algorithm for context-aware recommender systems, 2013.

D. Bouneffouf, Improving adaptation of ubiquitous recommander systems by using reinforcement learning and collaborative filtering, 2013.

D. Bouneffouf, 'l'apprentissage automatique', uné etape importante dans l'adaptation des systèmes d'informationàinformationà l'utilisateur, pp.427-428, 2013.

D. Bouneffouf, Mobile recommender systems methods: An overview, 2013.

D. Bouneffouf, Situation-aware approach to improve context-based recommender system, 2013.

D. Bouneffouf, Towards user profile modelling in recommender system, 2013.

D. Bouneffouf, \'etude des dimensions sp\'ecifiques du contexte dans un syst\eme de filtrage d'informations, 2014.

D. Bouneffouf, Recommandation mobile, sensible au contexte de contenus\'evolutifs: Contextuel-e-greedy, " arXiv preprint arXiv:1402, 1986.

D. Bouneffouf, A. Bouzeghoub, and A. L. Ganarski, Risk-Aware Recommender Systems, Neural Information Processing, pp.57-65, 2013.
DOI : 10.1007/978-3-642-42054-2_8

URL : https://hal.archives-ouvertes.fr/hal-01257882

D. Bouneffouf, A. Bouzeghoub, and A. L. Gançarski, A contextualbandit algorithm for mobile context-aware recommender system, Neural Information Processing, pp.324-331, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00753401

D. Bouneffouf, A. Bouzeghoub, and A. L. Gançarski, Exploration / Exploitation Trade-Off in Mobile Context-Aware Recommender Systems, AI 2012: Advances in Artificial Intelligence, pp.591-601, 2012.
DOI : 10.1007/978-3-642-35101-3_50

URL : https://hal.archives-ouvertes.fr/hal-00766969

D. Bouneffouf, A. Bouzeghoub, and A. L. Gancarski, Following the User's Interests in Mobile Context-Aware Recommender Systems: The Hybrid-e-greedy Algorithm, 2012 26th International Conference on Advanced Information Networking and Applications Workshops, pp.657-662, 2012.
DOI : 10.1109/WAINA.2012.200

D. Bouneffouf, A. Bouzeghoub, and A. L. Gançarski, Hybrid-?greedy for mobile context-aware recommender system, Advances in Knowledge Discovery and Data Mining, pp.468-479, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00744492

D. Bouneffouf, A. Bouzeghoub, and A. L. Gançarski, Contextual Bandits for Context-Based Information Retrieval, Neural Information Processing, pp.35-42, 2013.
DOI : 10.1007/978-3-642-42042-9_5

URL : https://hal.archives-ouvertes.fr/hal-01258059

D. Bouneffouf, A. Bouzeghoub, and A. L. Gançarski, Considering the high level critical situations in context-aware recommender systems, IMMoA'12: 2nd International Workshop on Information Management for Mobile Applications, pp.26-32, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00766995

A. Bouzeghoub, K. Do, and C. Lecocq, A Situation-Based Delivery of Learning Resources in Pervasive Learning, Second European Conference on Technology Enhanced Learning (EC-TEL 07). Creating New Learning Experiences on a Global ScaleCrete, Greece), pp.450-456, 2007.
DOI : 10.1007/978-3-540-75195-3_36

A. Bouzeghoub, K. Do, and L. Wives, Situation-Aware Adaptive Recommendation to Assist Mobile Users in a Campus Environment, 2009 International Conference on Advanced Information Networking and Applications, pp.503-509, 2009.
DOI : 10.1109/AINA.2009.120

URL : https://hal.archives-ouvertes.fr/hal-01368571

A. Bouzeghoub, C. Taconet, A. Jarraya, N. Do, C. et al., Complementarity of process-oriented and ontology-based context managers to identify situations, 2010 Fifth International Conference on Digital Information Management (ICDIM), 2010.
DOI : 10.1109/ICDIM.2010.5664620

URL : https://hal.archives-ouvertes.fr/hal-01354807

J. S. Breese, D. Heckerman, and C. Kadie, Empirical analysis of predictive algorithms for collaborative filtering, Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, UAI'98, pp.43-52, 1998.

P. Brusilovsky, Adaptive hypermedia, User Modeling and User-Adapted Interaction, vol.11, issue.1/2, pp.87-110, 2001.
DOI : 10.1023/A:1011143116306

R. Burke, Hybrid recommender systems: Survey and experiments, User Modeling and User-Adapted Interaction, vol.12, issue.4, pp.331-370, 2002.
DOI : 10.1023/A:1021240730564

G. Casella and R. Berger, Statistical Inference., Biometrics, vol.49, issue.1, 1990.
DOI : 10.2307/2532634

G. Castelli, A. Rosi, M. Mamei, and F. Zambonelli, A Simple Model and Infrastructure for Context-Aware Browsing of the World, Fifth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom'07), pp.229-238, 2007.
DOI : 10.1109/PERCOM.2007.4

D. D. Castro, A. Tamar, and S. Mannor, Policy gradients with variance related risk criteria, Proceedings of the International Conference on Machine Learning, 2012.

N. N. Chan, W. Gaaloul, and S. Tata, A Web Service Recommender System Using Vector Space Model and Latent Semantic Indexing, 2011 IEEE International Conference on Advanced Information Networking and Applications, pp.602-609, 2011.
DOI : 10.1109/AINA.2011.99

N. N. Chan, W. Gaaloul, and S. Tata, A Web Service Recommender System Using Vector Space Model and Latent Semantic Indexing, 2011 IEEE International Conference on Advanced Information Networking and Applications, pp.602-609, 2011.
DOI : 10.1109/AINA.2011.99

C. Vassilou, D. S. Martakos, and D. , A recommender system framework combining neural networks and collaborative filtering, Proceedings of the 5th WSEAS International Conference on Instrumentation, Measurement , Circuits and Systems, pp.285-290, 2006.

H. Chen, F. Perich, T. Finin, and A. Joshi, SOUPA: standard ontology for ubiquitous and pervasive applications, The First Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, 2004. MOBIQUITOUS 2004., 2004.
DOI : 10.1109/MOBIQ.2004.1331732

H. Chernoff, Sequential models for clinical trials, Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp.805-812, 1967.

W. Chu and S. Park, Personalized recommendation on dynamic content using predictive bilinear models, Proceedings of the 18th international conference on World wide web, WWW '09, pp.691-700, 2009.
DOI : 10.1145/1526709.1526802

C. Biancalana, F. Gasparetti, A. M. Sansonetti, and G. , Context-aware movie recommendation based on signal processing and machine learning, Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation, CAMRa '11, 2011.
DOI : 10.1145/2096112.2096114

D. Connolly, R. Khare, and A. Rifkin, The Evolution of Web Documents: The Ascent of XML, World Wide Web Journal, vol.2, issue.4, pp.119-128, 1997.

M. Endsley, Toward a Theory of Situation Awareness in Dynamic Systems, Human Factors: The Journal of the Human Factors and Ergonomics Society, vol.37, issue.1, pp.32-64, 1995.
DOI : 10.1518/001872095779049543

E. Even-dar, S. Mannor, and Y. Mansour, Pac bounds for multiarmed bandit and markov decision processes, Fifteenth Annual Conference on Computational Learning Theory, COLT '02, pp.255-270, 2002.

F. Santos-da-silva, L. G. Bressan, and G. , Personaltvware: An infrastructure to support the context-aware recommendation for personalized digital tv, International Journal of Computer Theory and Engineering, vol.4, issue.2, pp.131-136, 2012.

Y. Feng, T. Teng, and A. Tan, Modelling situation awareness for Context-aware Decision Support, Expert Systems with Applications, vol.36, issue.1, pp.455-463, 2009.
DOI : 10.1016/j.eswa.2007.09.061

Y. Ge, H. Xiong, A. Tuzhilin, X. , and K. , An energy-efficient mobile recommender system, Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '10, 2010.
DOI : 10.1145/1835804.1835918

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.224.7933

P. Geibel and F. Wysotzki, Risk-sensitive reinforcement learning applied to control under constraints, Journal. Artificial. Intelligence. Research, vol.24, pp.81-108, 2005.

J. Gittins, Multi-armed bandit allocation indices. Wiley-Interscience series in systems and optimization, 1989.
DOI : 10.1002/9780470980033

K. D. Glazebrook, On the evaluation of suboptimal strategies for families of alternative bandit processes, Journal of Applied Probability, vol.42, issue.03, pp.716-738, 1982.
DOI : 10.1007/BF01893487

D. Godoy and A. Amandi, User profiling in personal information agents: a survey, The Knowledge Engineering Review, vol.20, issue.04, pp.329-361, 2005.
DOI : 10.1017/S0269888906000397

J. Grudin, Partitioning digital worlds, Proceedings of the SIGCHI conference on Human factors in computing systems , CHI '01, pp.458-465, 2001.
DOI : 10.1145/365024.365312

A. Hans, D. Schneegaß, A. M. Schäfer, and S. Udluft, Safe exploration for reinforcement learning, European Symposium on Artificial Neural Networks, pp.143-148, 2008.

R. Hu and P. Pu, Acceptance issues of personality-based recommender systems, Proceedings of the third ACM conference on Recommender systems, RecSys '09, pp.221-224, 2009.
DOI : 10.1145/1639714.1639753

T. Ishikida and P. Varaiya, Multi-Armed bandit problem revisited, Journal of Optimization Theory and Applications, vol.9, issue.1, pp.113-154, 1994.
DOI : 10.1007/BF02191765

H. Kaspi and A. Mandelbaum, Multi-armed bandits in discrete and continuous time, The Annals of Applied Probability, vol.8, issue.4, 1998.
DOI : 10.1214/aoap/1028903380

J. Kim, U. Kang, L. , and J. , Content-Based Filtering for Music Recommendation Based on Ubiquitous Computing, pp.463-472, 2007.
DOI : 10.1007/978-0-387-44641-7_48

J. Kivinen and M. K. Warmuth, Exponentiated Gradient versus Gradient Descent for Linear Predictors, Information and Computation, vol.132, issue.1, 1995.
DOI : 10.1006/inco.1996.2612

D. Kostadinov, M. Bouzeghoub, and S. Lopes, Query rewriting based on user's profile knowledge, Bases de Données Avancées, BDA '07, 2007.

T. L. 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

URL : http://doi.org/10.1016/0196-8858(85)90002-8

K. Lakiotaki, N. F. Matsatsinis, and A. Tsoukias, Multicriteria User Modeling in Recommender Systems, IEEE Intelligent Systems, vol.26, issue.2, pp.64-76, 2011.
DOI : 10.1109/MIS.2011.33

L. Lam and S. Y. Suen, Application of majority voting to pattern recognition: an analysis of its behavior and performance, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol.27, issue.5, pp.553-568, 1997.
DOI : 10.1109/3468.618255

J. S. Lee and J. C. Lee, Context Awareness by Case-Based Reasoning in a Music Recommendation System, UCS, 2007.
DOI : 10.1007/978-3-540-76772-5_4

F. Lemos, R. Carmo, W. Viana, and R. Andrade, Towards a contextaware photo recommender system, Context-Aware Recommender System Workshops, 2012.

L. Li, W. Chu, J. Langford, and R. E. Schapire, A contextualbandit approach to personalized news article recommendation, Proceedings of the 19th international conference on World Wide Web, pp.661-670, 2010.

W. Li, X. Wang, R. Zhang, C. , and Y. , Exploitation and exploration in a performance based contextual advertising system, Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '10, pp.27-36, 2010.
DOI : 10.1145/1835804.1835811

Y. Liang, X. Wang, Y. , and Y. , One-armed bandit process with a covariate, Annals of the Institute of Statistical Mathematics, vol.65, issue.5, pp.993-1006, 2013.
DOI : 10.1007/s10463-013-0401-5

L. M. Lopez-lopez, J. J. Castro-schez, D. Vallejo-fernandez, A. , and J. , A Recommender System Based on a Machine Learning Algorithm for B2C Portals, 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, pp.524-531, 2009.
DOI : 10.1109/WI-IAT.2009.87

P. Lops, M. Degemmis, and G. Semeraro, Improving Social Filtering Techniques Through WordNet-Based User Profiles, Proceedings of the 11th international conference on User Modeling, UM '07, pp.268-277, 2007.
DOI : 10.1007/978-3-540-73078-1_30

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.269.6273

R. D. Luce, Individual choice behavior: A theoretical analysis, 2011.
DOI : 10.1037/14396-000

M. Montaner, B. L. De-la-rosa, and J. L. , Improving case representation and case base maintenance in recommender systems Advances in Case-Based Reasoning, Proceedings of the 6th European Conference on Case Based Reasoning, pp.234-248, 2002.

E. Mansour and H. Hopfner, Towards an XML-Based Query and Contextual Information Model in Context-Aware Mobile Information Systems, 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware, pp.574-579, 2009.
DOI : 10.1109/MDM.2009.99

S. I. Marcus, E. Fernández-gaucherand, D. Hernández-hernandez, S. Coraluppi, and P. Fard, Risk Sensitive Markov Decision Processes, Progress in Systems and Control Theory, pp.263-280, 1997.
DOI : 10.1007/978-1-4612-4120-1_14

J. Mccarthy, Situations, actions and causal laws, " tech. rep, pp.410-417, 1963.

J. Mccarthy and P. J. Hayes, Some Philosophical Problems from the Standpoint of Artificial Intelligence, Machine Intelligence, vol.4, pp.463-502, 1969.
DOI : 10.1016/B978-0-934613-03-3.50033-7

U. Meissen, S. Pfennigschmidt, A. Voisard, and T. Wahnfried, Context- and Situation-Awareness in Information Logistics, Proceedings of the 2004 international conference on Current Trends in Database Technology, EDBT'04, pp.335-344, 2004.
DOI : 10.1007/978-3-540-30192-9_33

N. Meuleau and P. Bourgine, Exploration of multi-state environments: Local measures and back-propagation of uncertainty, Machine Learning, pp.117-154, 1999.

M. Pazzani, D. Billsus, S. M. Wnek, and J. , Learning and revising user profiles: The identification of interesting web sites, Machine Learning, 1997.

S. E. Middleton, N. R. Shadbolt, D. Roure, and D. C. , Ontological user profiling in recommender systems, ACM Transactions on Information Systems, vol.22, issue.1, pp.54-88, 2004.
DOI : 10.1145/963770.963773

K. Minsoo, M. Lytras, E. Damiani, J. Carroll, R. Tennyson et al., A formal definition of situation towards situation-aware computing, Visioning and Engineering the Knowledge Society. A Web Science Perspective, vol.5736, pp.553-563, 2009.

D. Mladenic, Text-learning and related intelligent agents: a survey, IEEE Intelligent Systems, vol.14, issue.4, pp.44-54, 1999.
DOI : 10.1109/5254.784084

S. K. Mostefaoui, A context model based on UML and XML schema representations, 2008 IEEE/ACS International Conference on Computer Systems and Applications, pp.810-814, 2008.
DOI : 10.1109/AICCSA.2008.4493619

D. Mukhopadhyay, R. Dutta, A. Kundu, and R. Dattagupta, A Product Recommendation System Using Vector Space Model and Association Rule, 2008 International Conference on Information Technology, pp.279-282, 2008.
DOI : 10.1109/ICIT.2008.48

I. S. Nicholas and C. K. Nicholas, Combining content and collaboration in text filtering, Proceedings of the IJCAI99 Workshop on Machine Learning for Information Filtering, pp.86-91, 1999.

C. Niederée, A. Stewart, B. Mehta, and M. Hemmje, A Multi- Dimensional, Unified User Model for Cross-System Personalization, Workshop on Environment For Personlized Access Workshop, 2004.

C. Palmisano, A. Tuzhilin, and M. Gorgoglione, Using Context to Improve Predictive Modeling of Customers in Personalization Applications, IEEE Transactions on Knowledge and Data Engineering, vol.20, issue.11, pp.1535-1549, 2008.
DOI : 10.1109/TKDE.2008.110

G. I. Papadimitriou, A new approach to the design of reinforcement schemes for learning automata: stochastic estimator learning algorithms, IEEE Transactions on Knowledge and Data Engineering, vol.6, issue.4, pp.649-654, 1994.
DOI : 10.1109/69.298183

M. Pinheiro, Adaptation contextuelle et personnalisée de l'information de conscience de groupe au sein des systèmes d'information coopératifs, 2006.

D. Preuveneers, J. Den-bergh, D. Wagelaar, A. Georges, P. Rigole et al., Towards an Extensible Context Ontology for Ambient Intelligence, In: Proceedings of the Second European Symposium on Ambient Intelligence, pp.148-159, 2004.
DOI : 10.1007/978-3-540-30473-9_15

R. Reiter, Knowlege in action: logical foundations for specifying and implementing dynamical systems, 2001.

H. Robbins, Some Aspects of the Sequential Design of Experiments, Bulletin of the, pp.527-535, 1952.

E. Rojsattarat and N. Soonthornphisaj, Hybrid Recommendation: Combining Content-Based Prediction and Collaborative Filtering, Intelligent Data Engineering and Automated Learning, vol.2690, pp.337-344, 2003.
DOI : 10.1007/978-3-540-45080-1_44

G. Salton, A. Wong, Y. , and C. S. , A vector space model for automatic indexing, Communications of the ACM, vol.18, issue.11, pp.613-620, 1975.
DOI : 10.1145/361219.361220

B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, Item-based collaborative filtering recommendation algorithms, Proceedings of the tenth international conference on World Wide Web , WWW '01, pp.285-295, 2001.
DOI : 10.1145/371920.372071

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.167.7612

A. I. Schein, A. Popescul, L. H. Ungar, and D. M. Pennock, Methods and metrics for cold-start recommendations, Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval , SIGIR '02, pp.253-260, 2002.
DOI : 10.1145/564376.564421

G. Shani, L. Rokach, A. Meisels, L. Naamani, N. M. Piratla et al., Establishing User Profiles in the MediaScout Recommender System, 2007 IEEE Symposium on Computational Intelligence and Data Mining, pp.470-476, 2007.
DOI : 10.1109/CIDM.2007.368912

A. Stefani and C. Strappavara, Personalizing Access to Web Sites: The SiteIF Project, Proceedings of the 2nd Workshop on Adaptive Hypertext and Hypermedia HYPERTEXT'98, 1998.

X. Su and T. M. Khoshgoftaar, A Survey of Collaborative Filtering Techniques, Advances in Artificial Intelligence, 2009.
DOI : 10.1002/asi.10372

R. S. Sutton, A. G. Barto, and R. Learning, An Introduction (Adaptive Computation and Machine Learning), 1998.

T. Shiraki, C. Ito, and T. O. , Large scale evaluation of multimode recommender system using predicted contexts with mobile phone users, Context-aware recommender systems workshop, 2011.

A. Tamar, D. D. Castro, and S. Mannor, Policy evaluation with variance related risk criteria in markov decision processes, 1301.

M. Tokic, Adaptive epsilon-greedy exploration in reinforcement learning based on value differences, Proceedings of the 33rd annual German conference on Advances in Artificial Intelligence, pp.203-210, 2010.

J. N. Tsitsiklis, A Short Proof of the Gittins Index Theorem, The Annals of Applied Probability, vol.4, issue.1, pp.194-199, 1994.
DOI : 10.1214/aoap/1177005207

J. Vermorel and M. Mohri, Multi-armed Bandit Algorithms and Empirical Evaluation, Proceedings of the 16th European conference on Machine Learning, ECML'05, pp.437-448, 2005.
DOI : 10.1007/11564096_42

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.109.4518

X. Wang, D. Zhang, T. Gu, and H. Pung, Ontology based context modeling and reasoning using owl, Pervasive Computing and Communications Workshops, IEEE International Conference on, p.18, 2004.

R. Weber, On the Gittins Index for Multiarmed Bandits, The Annals of Applied Probability, vol.2, issue.4, pp.1024-1033, 1992.
DOI : 10.1214/aoap/1177005588

N. Webster and J. Mackechnie, Webster's New Twentieth Century Dictionary of the English Language, Based Upon the Broad Foundations Laid Down by Noah Webster. Collins World, 1975.

A. Wibowo, A. Handojo, and A. Halim, Application of Topic Based Vector Space Model with WordNet, 2011 International Conference on Uncertainty Reasoning and Knowledge Engineering, pp.133-136, 2011.
DOI : 10.1109/URKE.2011.6007864

M. Woodroofe, A One-Armed Bandit Problem with a Concomitant Variable, Journal of the American Statistical Association, vol.38, issue.368, pp.799-806, 1979.
DOI : 10.1080/01621459.1979.10481033

Z. Wu and M. Palmer, Verbs semantics and lexical selection, Proceedings of the 32nd annual meeting on Association for Computational Linguistics -, pp.133-138, 1994.
DOI : 10.3115/981732.981751

X. Tan and P. P. , A Contextual Item-Based Collaborative Filtering Technology, Intelligent Information Management, vol.04, issue.03, pp.85-88, 2012.
DOI : 10.4236/iim.2012.43013

URL : https://hal.archives-ouvertes.fr/hal-01074925

S. Yau, D. Huang, H. Gong, S. , and S. , Development and runtime support for situation-aware application software in ubiquitous computing environments, Proceedings of the 28th Annual International Computer Software and Applications Conference, 2004. COMPSAC 2004., pp.452-457, 2004.
DOI : 10.1109/CMPSAC.2004.1342878

S. Yau, Y. Wang, D. Huang, I. , and H. , Situationaware contract specification language for middleware for ubiquitous computing, Proceedings of the 9th IEEE Workshop on Future Trends of Distributed Computing Systems (FTDCS 2003, pp.93-99, 2003.

J. Ye, S. Dobson, and S. Mckeever, Situation identification techniques in pervasive computing: A review, Pervasive and Mobile Computing, vol.8, issue.1, pp.36-66, 2012.
DOI : 10.1016/j.pmcj.2011.01.004

X. Yu, Y. Li, X. Wang, and K. Zhao, An Autonomous Robust Fault Tolerant Control System, 2006 IEEE International Conference on Information Acquisition, pp.1191-1196, 2006.
DOI : 10.1109/ICIA.2006.305916

D. Zhang, Collaborative Filtering Recommendation Algorithm Based on User Interest Evolution, of Advances in Intelligent and Soft Computing, pp.279-283, 2012.
DOI : 10.1007/978-3-642-25986-9_44

Y. Zhang and J. Koren, Efficient bayesian hierarchical user modeling for recommendation system, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '07, pp.47-54, 2007.
DOI : 10.1145/1277741.1277752