P. B. Kantor, F. Ricci, L. Rokach, and B. Shapira, Recommender systems handbook, Recommender systems handbook, p.848, 2011.

K. Zhou, S. Yang, and H. Zha, Functional matrix factorizations for cold-start recommendation, Proceedings of the 34th international ACM SIGIR conference on Research and development in Information, SIGIR '11, pp.315-324, 2011.
DOI : 10.1145/2009916.2009961

A. Broder and M. Mitzenmacher, Network Applications of Bloom Filters: A Survey, Internet Mathematics, vol.1, issue.4, pp.485-509, 2004.
DOI : 10.1080/15427951.2004.10129096

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, pp.43-52, 1998.

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

J. Karim, Hybrid system for personalized recommendations, 2014 IEEE Eighth International Conference on Research Challenges in Information Science (RCIS), pp.1-6, 2014.
DOI : 10.1109/RCIS.2014.6861080

E. Peis, J. M. Castillo, and J. Delgado-lópez, Semantic recommender systems. analysis of the state of the topic, Hipertext. net, vol.6, pp.1-5, 2008.

R. Dahimene, C. Constantin, and C. Du-mouza, RecLand, Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM '14, pp.2063-2065, 2014.
DOI : 10.1145/2661829.2661850
URL : https://hal.archives-ouvertes.fr/hal-01126503

I. Cantador, P. Brusilovsky, and T. Kuflik, 2nd workshop on information heterogeneity and fusion in recommender systems, Proceedings of the 5th ACM conference on Recommender systems, 2011.

B. Sarwar, G. Karypis, J. 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

D. Lemire and A. Maclachlan, Slope one predictors for online ratingbased collaborative filtering, SDM, pp.1-5, 2005.

T. Hofmann, Collaborative filtering via gaussian probabilistic latent semantic analysis, Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval , SIGIR '03, pp.259-266, 2003.
DOI : 10.1145/860435.860483

Y. Koren, The bellkor solution to the netflix grand prize Netflix prize documentation, 2009.

Y. Koren, R. 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

Y. Koren and R. Bell, Advances in collaborative filtering, Recommender Systems Handbook, pp.145-186, 2011.

A. Tiroshi, T. Kuflik, J. Kay, and B. Kummerfeld, Recommender systems and the social web Advances in User Modeling, pp.60-70, 2012.

R. Boim, T. Milo, and S. Novgorodov, DiRec: Diversified recommendations for semantic-less Collaborative Filtering, 2011 IEEE 27th International Conference on Data Engineering, pp.1312-1315, 2011.
DOI : 10.1109/ICDE.2011.5767942

Y. B. Fernández, J. J. Arias, M. L. Nores, A. G. Solla, and M. R. Cabrer, Avatar: An improved solution for personalized tv based on semantic inference Consumer Electronics, IEEE Transactions on, vol.52, issue.1, pp.223-231, 2006.

P. Pan, C. Wang, G. Horng, and S. Cheng, The development of an Ontology-Based Adaptive Personalized Recommender System, 2010 International Conference on Electronics and Information Engineering, pp.1-76, 2010.
DOI : 10.1109/ICEIE.2010.5559842

G. Katz, N. Ofek, B. Shapira, L. Rokach, and G. Shani, Using Wikipedia to boost collaborative filtering techniques, Proceedings of the fifth ACM conference on Recommender systems, RecSys '11, pp.285-288, 2011.
DOI : 10.1145/2043932.2043984

D. Werner, C. Cruz, and C. Nicolle, Ontology-based recommender system of economic articles, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00778528

M. G. Vozalis and K. G. Margaritis, Using SVD and demographic data for the enhancement of generalized Collaborative Filtering, Information Sciences, vol.177, issue.15, pp.3017-3037, 2007.
DOI : 10.1016/j.ins.2007.02.036

B. Mobasher, X. Jin, and Y. Zhou, Semantically Enhanced Collaborative Filtering on the Web, Web Mining: From Web to Semantic Web, pp.57-76, 2004.
DOI : 10.1007/978-3-540-30123-3_4

M. Pozo, R. Chiky, and E. Métais, Extraction de l'intérêt implicite des utilisateurs dans les attributs des items pour améliorer les systèmes de recommandations, " in 15ème conférence internationale sur l'extraction et la gestion des connaissances, pp.1-6, 2015.

S. Geravand and M. Ahmadi, An efficient and scalable plagiarism checking system using Bloom filters, Computers & Electrical Engineering, vol.40, issue.6, pp.1789-1800, 2014.
DOI : 10.1016/j.compeleceng.2014.06.003

N. Jain, M. Dahlin, and R. Tewari, Using bloom filters to refine web search results, WebDB, pp.25-30, 2005.

B. H. Bloom, Space/time trade-offs in hash coding with allowable errors, Communications of the ACM, vol.13, issue.7, pp.422-426, 1970.
DOI : 10.1145/362686.362692

S. J. Swamidass and P. Baldi, Mathematical Correction for Fingerprint Similarity Measures to Improve Chemical Retrieval, Journal of Chemical Information and Modeling, vol.47, issue.3, pp.952-964, 2007.
DOI : 10.1021/ci600526a

D. Guo, J. Wu, H. Chen, Y. Yuan, and X. Luo, The Dynamic Bloom Filters, IEEE Transactions on Knowledge and Data Engineering, vol.22, issue.1, pp.120-133, 2010.
DOI : 10.1109/TKDE.2009.57

P. S. Almeida, C. Baquero, N. Preguiça, and D. Hutchison, Scalable Bloom Filters, Information Processing Letters, vol.101, issue.6, pp.255-261, 2007.
DOI : 10.1016/j.ipl.2006.10.007