G. Adomavicius and Y. Kwon, Overcoming accuracy-diversity tradeoff in recommender systems : A variance-based approach, Proceedings of the 18th Workshop on Information Technology and Systems (WITS'08), 2008.

G. Adomavicius and Y. Kwon, Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques, IEEE Transactions on Knowledge and Data Engineering, vol.24, issue.5, pp.896-911, 2012.
DOI : 10.1109/TKDE.2011.15
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.459.8174

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, pp.5-14, 2009.
DOI : 10.1145/1498759.1498766
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.188.1018

A. Belén-barragáns-martínez, E. Costa-montenegro, J. C. Burguillo, M. Rey-lópez, and A. Fernando, A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition, Information Sciences, vol.180, issue.22, pp.4290-4311, 2010.
DOI : 10.1016/j.ins.2010.07.024

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, pp.739-744, 2011.
DOI : 10.1145/2063576.2063684

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

L. Candillier, M. Chevalier, D. Dudognon, and J. Mothe, Diversité de recommandations : ApplicationàApplication`Applicationà une plateforme de blogs etévaluationet´etévaluation, Conférence francophone en Recherche d'Information et Applications (CORIA'13), pp.269-276, 2013.

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'accèsaccès`accèsà l'information, Thèse de doctorat, 2008.

A. Sylvain-castagnos, A. Brun, and . Boyer, When diversity is needed... but not expected, Proceedings of the International Conference on Advances in Information Mining and Management (IMMM'13), 2013.

N. Sylvain-castagnos, P. Jones, and . Pu, Recommenders' influence on buyers' decision process, proc. of the 3rd ACM Conference on Recommender Systems (RecSys'09), pp.361-364, 2009.

N. Sylvain-castagnos, P. Jones, and . Pu, Eye-tracking product recommenders' usage, Proceedings of the 4th ACM Conference on Recommender Systems, 2010.

L. Chen and P. Pu, A cross-cultural user evaluation of product recommender interfaces, Proceedings of the 2008 ACM conference on Recommender systems, RecSys '08, pp.75-82, 2008.
DOI : 10.1145/1454008.1454022

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

D. B. 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.

D. Fleder and K. Hosanagar, Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity, Management Science, vol.55, issue.5, pp.697-712, 2009.
DOI : 10.1287/mnsc.1080.0974

M. Ge, F. Gedikli, and D. Jannach, Placing high-diversity items in top-n recommendation lists, Workshop on Intelligent Techniques for Web Personalization and Recommender Systems (ITWP'11), 2011.

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. He, K. Balog, K. Hofmann, and E. Meij, Maarten de Rijke, Manos Tsagkias et Wouter Weerkamp Heuristic ranking and diversification of web documents, Text REtrieval Conference, 2009.

J. L. Herlocker, J. A. Konstan, L. G. Terveen, J. , and T. , Evaluating collaborative filtering recommender systems, ACM Transactions on Information Systems, vol.22, issue.1, pp.5-53, 2004.
DOI : 10.1145/963770.963772
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.78.8384

R. Hu and P. Pu, Helping users perceive recommendation diversity, Proceedings of the 1st International Workshop on Novelty and Diversity in Recommender Systems (DiveRS'11), 2011.

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, 2010.

R. Kaptein, M. Koolen, and J. Kamps, Result diversity and entity ranking experiments : Anchors, links, text and wikipedia, Proceedings of the Eighteenth Text REtrieval Conference, 2009.

L. Neal-kiritkumar, Evaluating Collaborative Filtering Over Time, 2010.

L. Mcginty and B. Smyth, On the Role of Diversity in Conversational Recommender Systems, International Conference on Case-Based Reasoning (ICCBR'03), 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 (ECCBR'02), pp.219-233, 2002.
DOI : 10.1007/3-540-46119-1_17

P. Pu, L. Chen, and R. Hu, Evaluating recommender systems from the user???s perspective: survey of the state of the art, User Modeling and User-Adapted Interaction, vol.11, issue.1???2, pp.317-355, 2012.
DOI : 10.1007/s11257-011-9115-7

F. Radlinski, P. N. Bennett, B. Carterette, and T. Joachims, 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

F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Recommender Systems Handbook, 2011.

A. Said, B. Kille, J. Brijnesh, S. Jain, and . Albayrak, Increasing diversity through furthest neighbor-based recommendation, Proceedings of the WSDM'12 Workshop on Diversity in Document Retrieval, 2012.

R. Luis and T. Santos, Explicit Web Search Result Diversification, 2013.

B. M. Sarwar, G. Karypis, J. A. Konstan, and J. , 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

P. Sawers, Remember netflix's $1m algorithm contest ? well, here's why it didn't use the winning entry. thenextweb.com/mediaremember-netflixs- 1m-algorithm-contest-well-heres-why-it-didnt-use-the-winning-entry, p.2012, 2012.

J. B. Schafer, J. A. Konstan, and J. , Meta-recommendation systems, Proceedings of the eleventh international conference on Information and knowledge management , CIKM '02, pp.43-51, 2002.
DOI : 10.1145/584792.584803

V. Schickel and B. Faltings, Using an ontologcial a-priori score to infer user's preferences, Workshop on Recommender Systems Conjunction with the 17th European Conference on Artificial Intelligence Riva del Garda, 2006.

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

J. Sill, G. Takacs, L. Mackey, and D. Lin, Feature-weighted linear stacking . Netflix prize report, 2009.

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

A. Tscher and M. Jahrer, The bigchaos solution to the netflix grand prize. Netflix prize report, 2009.

S. Vargas and P. Castells, Rank and relevance in novelty and diversity metrics for recommender systems, Proceedings of the fifth ACM conference on Recommender systems, RecSys '11, 2011.
DOI : 10.1145/2043932.2043955

S. Wan, Y. Xue, X. Yu, F. Guan, Y. Liu et al., Ictnet at web track 2011 diversity track, Text REtrieval Conference, 2011.

Y. Koren, R. M. 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
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.147.8295

C. Yu, V. S. Laks, S. Lakshmanan, and . Amer-yahia, Recommendation Diversification Using Explanations, 2009 IEEE 25th International Conference on Data Engineering, pp.1299-1302, 2009.
DOI : 10.1109/ICDE.2009.225
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.152.2206

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