G. Adomavicius, S. Sankaranarayanan, S. Sen, and A. Tuzhilin, Incorporating contextual information in recommender systems using a multidimensional approach, Transaction on Information System, pp.103-145, 2005.
DOI : 10.1145/1055709.1055714

G. Adomavicius and A. Tuzhilin, Contexte-aware recommender systems, Recommender Systems Hndbook, pp.217-253, 2001.

R. Agrawal, S. Gollapudi, A. Halverson, and S. Ieong, Diversifying search results, Proceedings of the Second ACM International Conference on Web Search and Data Mining, WSDM '09, 2009.
DOI : 10.1145/1498759.1498766

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.188.1018

M. Bazire and P. Brezillon, Understanding Context Before Using It, Proceedings of the 5th International Conference on Modeling and Using Context, ICMUC'05, 2005.
DOI : 10.1007/11508373_3

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.218.8755

R. Boim, T. Milo, and S. Novgorodov, Diversification and refinement in collaborative filtering recommender, Proceedings of the 20th ACM international conference on Information and knowledge management, CIKM '11, 2011.
DOI : 10.1145/2063576.2063684

G. Bonnin, Vers des systèmes de recommandation robustes pour la navigation Web : inspiration de la modélisation statistique du langage. These, 2010.

K. Bradley and B. Smith, Improving recommendation diversity, irish Conference on Artificial Intelligence and Cognitive Science, AICS'01, pp.85-94, 2001.

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. Brown and X. Chen, Context-aware applications: from the laboratory to the marketplace, IEEE, IEEE'97, pp.58-64, 1997.
DOI : 10.1109/98.626984

R. Burke, Hybrid recommender systems : Survey and experiments. User Modeling and User-Adapted Interaction, pp.331-370, 2002.

S. Castagnos, Modélisation de comportements et apprentissage stochastique non supervisé de stratégies d'interactions sociales au sein de systèmes temps réel de recherche et d'accèsaccèsà l'information. These, 2008.

S. Castagnos, A. Brun, and A. Boyer, Utilité et perception de la diversité dans les systèmes de recommandation, CORIA 2013 -10ème COnférence en Recherche d'Information et Applications, 2013.

S. Castagnos, A. Brun, and A. Boyer, La diversité : entre besoin et méfiance dans les systèmes de recommandation, Information Interaction Intelligence, p.14, 2014.

S. Castagnos, N. Jones, and P. Pu, Eye-tracking product recommenders' usage, Proceedings of the fourth ACM conference on Recommender systems, RecSys '10, pp.29-36, 2010.
DOI : 10.1145/1864708.1864717

URL : http://infoscience.epfl.ch/record/150716

. Celma and . Oscar, Music Recommendation and Discovery in the Long Tail, 2008.

S. H. Chee, J. Han, W. , and K. , RecTree: An Efficient Collaborative Filtering Method, Proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery, DaWaK '01, pp.141-151, 2001.
DOI : 10.1007/3-540-44801-2_15

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.17.9198

G. Chen, L. V. Chen, T. Kuflik, D. Chin, F. Ricci et al., Recommendation Based on Contextual Opinions, Dimitrova, User Modeling, Adaptation, and Personalization, pp.61-73, 2014.
DOI : 10.1007/978-3-319-08786-3_6

L. Chen, M. De-gemmis, A. Felfernig, P. Lops, F. Ricci et al., Human Decision Making and Recommender Systems, ACM Transactions on Interactive Intelligent Systems, vol.3, issue.3, pp.1-17, 2013.
DOI : 10.1145/2533670.2533675

L. Chen and P. Pu, Critiquing-based recommenders: survey and emerging trends, User Modeling and User-Adapted Interaction, vol.39, issue.10, pp.125-150, 2012.
DOI : 10.1007/s11257-011-9108-6

C. L. Clarke, M. Kolla, G. V. Cormack, O. Vechtomova, A. Ashkan et al., Novelty and diversity in information retrieval evaluation, Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '08, pp.659-666, 2008.
DOI : 10.1145/1390334.1390446

D. Cosley, S. K. Lam, I. Albert, J. A. Konstan, and J. Riedl, Is seeing believing?, Proceedings of the conference on Human factors in computing systems , CHI '03, pp.585-592, 2003.
DOI : 10.1145/642611.642713

F. Davis, Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology, MIS Quarterly 13, ICCBR'01, pp.319-340, 1989.
DOI : 10.2307/249008

D. Department and D. Bridge, Product recommendation systems : A new direction, Workshop on CBR in Electronic Commerce at The international COnference on Case-Based Reasoning, ICCBR'01, pp.79-86, 2001.

A. Dey, G. Abowd, and D. Salber, A conceptual framework and a toolkit for supporting the rapid prototype of context-aware appllications, Human-Computer Interaction, HCI'01, pp.97-166, 2001.

M. Foulonneau, V. Groues, Y. Naudet, C. , and M. , Recommandeurs et diversité : Exploitation de la longue tra??netra??ne et diversité des listes de recommandations, Les systèmes de recommandation. Hermès, 2014.

D. Goldberg, D. Nichols, B. M. Oki, T. , and D. , Using collaborative filtering to weave an information tapestry, Communications of the ACM, vol.35, issue.12, pp.3561-70, 1992.
DOI : 10.1145/138859.138867

G. Häubl and K. B. Murray, Preference Construction and Persistence in Digital Marketplaces: The Role of Electronic Recommendation Agents, Journal of Consumer Psychology, vol.13, issue.1-2, pp.75-91, 2003.
DOI : 10.1207/S15327663JCP13-1&2_07

J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl, An algorithmic framework for performing collaborative filtering, Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval , SIGIR '99, pp.230-237, 1999.
DOI : 10.1145/312624.312682

M. Jahrer, A. Tscher, and R. Legenstein, Combining predictions for accurate recommender systems, Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '10, pp.693-702, 2010.
DOI : 10.1145/1835804.1835893

N. Jones, User Perceived Qualities and Acceptance of Recommender Systems : The Role of Diversity, 2010.

N. Jones, A. Brun, A. Boyer, and A. Hamad, An Exploratory Work in Using Comparisons Instead of Ratings, of Lecture Notes in Business Information Processing, pp.184-195, 2011.
DOI : 10.1016/S0001-6918(99)00050-5

N. Jones and P. Pu, User technology adoption issues in recommender systems, Proceedings of the 2007 Networking and Electronic Commerce Research Conference, pp.379-394, 2007.

N. Lathia, Evaluating Collaborative Filtering Over Time, 2010.

N. Lathia, S. Hailes, L. Capra, and X. Amatriain, Temporal diversity in recommender systems, Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval, SIGIR '10, pp.210-217, 2010.
DOI : 10.1145/1835449.1835486

D. Leake and R. Scherle, Towards context-based search engine selection, Proceedings of the 6th international conference on Intelligent user interfaces , IUI '01, pp.109-112, 2001.
DOI : 10.1145/359784.360301

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1.8827

S. Lee, J. Yang, and S. Park, Discovery of Hidden Similarity on Collaborative Filtering to Overcome Sparsity Problem, Discovery Science, pp.396-402, 2004.
DOI : 10.1007/978-3-540-30214-8_36

L. Huillier, A. Castagnos, S. Boyer, and A. , Understanding usages by modeling diversity over time, UMAP, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01108990

L. Mcginty and B. Smyth, On the Role of Diversity in Conversational Recommender Systems, Proceedings of the Fifth International Conference on Case-Based Reasoning, pp.276-290, 2003.
DOI : 10.1007/3-540-45006-8_23

S. M. Mcnee, J. Riedl, and J. A. Konstan, Being accurate is not enough, CHI '06 extended abstracts on Human factors in computing systems, CHI EA '06, pp.1097-1101, 2006.
DOI : 10.1145/1125451.1125659

D. Mcsherry, Diversity-Conscious Retrieval, Proceedings of the 6th European Conference on Advances in Case-Based Reasoning, pp.219-233, 2002.
DOI : 10.1007/3-540-46119-1_17

T. Murakami, K. Mori, and R. Orihara, Metrics for Evaluating the Serendipity of Recommendation Lists, Proceedings of the 2007 Conference on New Frontiers in Artificial Intelligence, JSAI'07, pp.40-46, 2008.
DOI : 10.1007/978-3-540-78197-4_5

K. Oku, J. Nakajima, and S. Uemura, Context-aware svm for contet-dependend information recommendation, Proceedings of the 7th International Conference on Mobile Data Management, p.109, 2006.
DOI : 10.1109/mdm.2006.56

K. Onuma, H. T. Faloutsos, and C. , TANGENT, Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '09, pp.657-666, 2009.
DOI : 10.1145/1557019.1557093

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.201535-1549, 2008.
DOI : 10.1109/TKDE.2008.110

Y. J. Park and A. Tuzhilin, The long tail of recommender systems and how to leverage it, Proceedings of the 2008 ACM conference on Recommender systems, RecSys '08, pp.11-18, 2008.
DOI : 10.1145/1454008.1454012

M. J. Pazzani and D. Billsus, The adaptive web. chapter Content-based Recommendation Systems, pp.325-341, 2007.

P. Pu, M. Zhou, C. , and S. , Critiquing recommenders for public taste products, Proceedings of the third ACM conference on Recommender systems, RecSys '09, pp.249-252, 2009.
DOI : 10.1145/1639714.1639760

F. Radlinski, P. N. Bennett, B. Carterette, J. , and T. , Redundancy, diversity and interdependent document relevance, ACM SIGIR Forum, vol.43, issue.2, pp.46-52, 2009.
DOI : 10.1145/1670564.1670572

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.154.4212

P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, GroupLens, Proceedings of the 1994 ACM conference on Computer supported cooperative work , CSCW '94, pp.175-186, 1994.
DOI : 10.1145/192844.192905

A. Said, B. Kille, J. Brijnesh, A. , and S. , Increasing diversity throught furhest neighbor-based recommandation, Proceedings of the Workshop on Diversity in Document Retrieval, WSDM'12, 2012.

J. Schafer, J. Konstan, R. , and J. , Meta-recommandations systems : user-controlled integration of diverse recommendations, International Conference on Information And Knowledge Management, pp.43-51, 2002.

B. Schilit and M. Theimer, Disseminating active map information to mobile hosts, IEEE network, pp.22-32, 1994.
DOI : 10.1109/65.313011

U. Shardanand and P. Maes, Social information filtering, Proceedings of the SIGCHI conference on Human factors in computing systems, CHI '95, pp.210-217, 1995.
DOI : 10.1145/223904.223931

H. Shimazu, Expertclerk : Navigation shoppers buying process with the combination of asking and proposing, Proceedings of the International Joint Conferencee on Artificial Intelligence, IJCAI'01, pp.1443-1450, 2001.

A. Sieg, B. Mabasher, and R. Burke, Representing context in web search with ontological user profiles, Proceedings of the 6th International Conference on Modeling and Using Context, 2007.

B. Smyth and P. Mcclave, Similarity vs. Diversity, Proceedings of the 4th International Conference on Case-Based Reasoning : Case-Based Reasoning Research and Development, ICCBR '01, pp.347-361, 2001.
DOI : 10.1007/3-540-44593-5_25

K. Sonam and G. Puneet, An efficient solution of travelling salesman problem using genetic algorithm, In International Journal of Advanced Research in Computer Science and Software Engineering, vol.4, issue.5, 2014.

X. Su and T. M. Khoshgoftaar, A Survey of Collaborative Filtering Techniques, Advances in Artificial Intelligence, vol.46, issue.2, pp.2-4, 2009.
DOI : 10.1002/asi.10372

L. Ungar and D. Foster, Clustering methods for collaborative filtering, Proceedings of the Workshop on Recommendation Systems, 1998.

N. Webster, Webster's new twentieth century dictionary of the english language, 1980.

M. Zhang and N. Hurley, Avoiding monotony, Proceedings of the 2008 ACM conference on Recommender systems, RecSys '08, pp.123-130, 2008.
DOI : 10.1145/1454008.1454030

C. Ziegler, S. M. Mcnee, J. A. Konstan, and G. Lausen, Improving recommendation lists through topic diversification, Proceedings of the 14th international conference on World Wide Web , WWW '05, pp.22-32, 2005.
DOI : 10.1145/1060745.1060754

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.144.835