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.5-5, 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. B. Barragáns-martínez, E. Costa-montenegro, J. C. Burguillo, M. Rey-lópez, F. A. Mikic-fonte et al., 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

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'accès à l'information, Thèse de doctorat, 2008.

S. Castagnos, N. Jones, and P. Pu, Influence on Buyers' Decision Process », In proc, of the 3rd ACM Conference on Recommender Systems (RecSys'09), pp.361-364, 2009.

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

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

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

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. Murray, « Preference Construction and Persistence in Digital Marketplaces: The Role of Electronic Recommendation Agents, Journal of Consumer Psychology, vol.13, pp.1-1, 2003.

J. L. Herlocker, J. A. Konstan, L. G. Terveen, . John, and T. Riedl, Evaluating collaborative filtering recommender systems, Evaluating collaborative filtering recommender systems, 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.

N. Jones, User Perceived Qualities and Acceptance of Recommender Systems, 2010.

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

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

A. Said, B. Kille, B. J. Jain, and S. Albayrak, « Increasing Diversity Through Furthest Neighbor- Based Recommendation, Proceedings of the WSDM'12 Workshop on Diversity in Document Retrieval, 2012.

B. Sarwar, G. Karypis, J. Konstan, and J. Reidl, 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, Netflix's $1m algorithm contest? Well, here's why it didn't use the winning entry », http://thenextweb.com/mediaremember-netflixs-1m-algorithm- contest-well-heres-why-it-didnt-use-the-winning-entry, p.2012, 2012.

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, Italy, 2006.

J. Sill, G. Takacs, L. Mackey, and D. Lin, Feature-Weighted Linear Stacking, Netflix prize report, 2009.

A. Töscher and M. Jahrer, The BigChaos Solution to the Netflix Grand Prize, Netflix prize report, 2009.

C. Yu and L. V. Lakshmanan, Amer-Yahia S., « Recommendation Diversification Using Explanations, Proceedings of the 2009 IEEE International Conference on Data Engineering (ICDE'09), pp.1299-1302, 2009.

M. Zhang and N. Hurley, Improving the Diversity of Recommendation Lists, Proceedings of the 2nd ACM Recommender Systems, pp.123-130, 2008.

C. Ziegler, S. Mcnee, J. 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