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. Aggarwal, An introduction to outlier analysis, Outlier Analysis, pp.1-40, 2013.

A. Bellogín, P. Castells, and I. Cantador, Predicting the Performance of Recommender Systems: An Information Theoretic Approach, Proc. of the Third Int. Conf. on Advances in Information Retrieval Theory, pp.27-39, 2011.
DOI : 10.1145/1361684.1361689

A. Bellogín, A. Said, and A. De-vries, The Magic Barrier of Recommender Systems ??? No Magic, Just Ratings, Proc. of the 22nd Conf. on User Modelling, Adaptation and Personalization (UMAP), 2014.
DOI : 10.1007/978-3-319-08786-3_3

D. Billsus and M. J. Pazzani, Learning collaborative information filters, Proc. of the Fifteenth Int. Conf. on Machine Learning, ICML '98, pp.46-54, 1998.

J. Bobadilla, F. Ortega, H. , and A. , A collaborative filtering similarity measure based on singularities, Information Processing & Management, vol.48, issue.2, pp.204-217, 2012.
DOI : 10.1016/j.ipm.2011.03.007

J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez, Recommender systems survey, Knowledge-Based Systems, vol.46, pp.109-132, 2013.
DOI : 10.1016/j.knosys.2013.03.012

S. Castagnos, A. Brun, and A. Boyer, When diversity is needed... but not expected! In IMMM 2013, The Third Int, Conf. on Advances in Information Mining and Management, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00931805

D. Prete, L. Capra, and L. , differs: A mobile recommender service, Proc. of the 2010 Eleventh Int. Conf. on Mobile Data Management, MDM '10, pp.21-26, 2010.

M. Ekstrand, Towards Recommender Engineering. Tools and Exp. for Identifying Recommender Differences, 2014.

M. Ekstrand and J. Riedl, When recommenders fail, Proceedings of the sixth ACM conference on Recommender systems, RecSys '12, pp.233-236, 2012.
DOI : 10.1145/2365952.2366002

M. Ghazanfar and A. Bennett, fulfilling the needs of gray-sheep users in recommender systems, a clustering solution, 2011 Int. Conf. on Information Systems and Computational Intelligence. Event Dates, pp.18-20, 2011.

D. Goldberg, D. Nichols, B. Oki, T. , and D. , Using collaborative filtering to weave an information tapestry, Communications of the ACM, vol.35, issue.12, pp.61-70, 1992.
DOI : 10.1145/138859.138867

M. Grcar, D. Mladenic, and M. Grobelnik, Data quality issues in collaborative filtering, Proc. of ESWC-2005 Workshop on End User Aspects of the Semantic Web, 2005.

J. Griffith, C. O-'riordan, and H. Sorensen, Investigations into user rating information and predictive accuracy in a collaborative filtering domain, Proceedings of the 27th Annual ACM Symposium on Applied Computing, SAC '12, pp.937-942, 2012.
DOI : 10.1145/2245276.2245458

D. M. Hawkins, Identification of outliers, 1980.
DOI : 10.1007/978-94-015-3994-4

C. Haydar, A. Roussanaly, and A. Boyer, Clustering Users to Explain Recommender Systems??? Performance Fluctuation, Foundations of Intelligent Systems, pp.357-366, 2012.
DOI : 10.1007/978-3-642-34624-8_41

URL : https://hal.archives-ouvertes.fr/hal-00777218

J. Herlocker, J. Konstan, L. Terveen, and J. Riedl, Evaluating collaborative filtering recommender systems, ACM Transactions on Information Systems, vol.22, issue.1, 2004.
DOI : 10.1145/963770.963772

Y. Hu, Y. Koren, and C. Volinsky, Collaborative Filtering for Implicit Feedback Datasets, 2008 Eighth IEEE International Conference on Data Mining, pp.263-272, 2008.
DOI : 10.1109/ICDM.2008.22

R. Ormándi, I. Hegeds, K. Csernai, and M. Jelasity, Towards Inferring Ratings from User Behavior in BitTorrent Communities, 2010 19th IEEE International Workshops on Enabling Technologies: Infrastructures for Collaborative Enterprises, pp.217-222, 2010.
DOI : 10.1109/WETICE.2010.41

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. I. Schein, A. Popescul, L. H. Ungar, and D. Pennock, Generative models for cold-start recommendations, Proc. of the 2001 SIGIR workshop on recommender systems, 2001.

V. Schickel-zuber and B. Faltings, Overcoming Incomplete User Models in Recommendation Systems Via an Ontology, Proc. of the 7th Int. Conf. on Knowledge Discovery on the Web, WebKDD'05, pp.39-57, 2006.
DOI : 10.1007/11891321_3

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