F. Constantin, I. Aliferis, and . Tsamardinos, Algorithms for large-scale local causal discovery and feature selection in the presence of limited sample or large causal neighbourhoods, 2002.

N. B. Mouna-ben-ishak, P. Amor, and . Leray, A relational bayesian network-based recommender system architecture, Proceedings of the 5th International Conference on Modeling, Simulation and Applied Optimization, p.2013, 2013.

S. John, D. Breese, C. Heckerman, and . Kadie, Empirical analysis of predictive algorithms for collaborative filtering, Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, pp.43-52, 1998.

D. Maxwell and C. , Learning bayesian networks is np-complete, Learning from data, pp.121-130, 1996.

F. Gregory and . Cooper, The computational complexity of probabilistic inference using bayesian belief networks (research note), Artif. Intell, vol.42, issue.2-390, pp.393-4050004, 1990.

R. Daly, Q. Shen, and S. Aitken, Learning Bayesian networks: approaches and issues, The Knowledge Engineering Review, vol.10, issue.02, pp.99-157, 2011.
DOI : 10.1007/BFb0053999

N. Friedman, L. Getoor, D. Koller, and A. Pfeffer, Learning probabilistic relational models, International Joint Conference on Artificial Intelligence, pp.1300-1309, 1999.

Y. Gao, H. Qi, J. Liu, and D. Liu, A recommendation algorithm combining user gradebased collaborative filtering and probabilistic relational models, Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery, pp.67-71, 2007.

L. Getoor, Learning statistical models from relational data, Proceedings of the 2011 international conference on Management of data, SIGMOD '11, 2001.
DOI : 10.1145/1989323.1989451

L. Getoor and M. Sahami, Using probabilistic relational models for collaborative filtering, Proc. Workshop Web Usage Analysis and User Profiling (WEBKDD'99). Citeseer, 1999.

L. Getoor, N. Friedman, D. Koller, and B. Taskar, Learning probabilistic models of relational structure, Proceedings of the Eighteenth International Conference on Machine Learning, ICML '01, pp.170-177, 2001.

N. Good, B. Schafer, A. Joseph, A. Konstan, B. Borchers et al., Combining collaborative filtering with personal agents for better recommendations, Proceedings of the National Conference on Artificial Intelligence, pp.439-446, 1999.

L. Jonathan, . Herlocker, A. Joseph, . Konstan, G. Loren et al., Evaluating collaborative filtering recommender systems, ACM Transactions on Information Systems (TOIS), vol.22, issue.1, pp.5-53, 2004.

T. Hofmann and J. Puzicha, Latent class models for collaborative filtering, Proceedings of the 16th International Joint Conference on Artificial Intelligence - IJ- CAI'99, pp.688-693, 1999.

Z. Huang, H. Zeng, and . Chen, A Unified Recommendation Framework Based on Probabilistic Relational Models, Fourteenth Annual Workshop on Information Technologies and Systems (WITS), pp.8-13, 2004.
DOI : 10.2139/ssrn.906513

Z. Huang, H. Zeng, and . Chen, A Unified Recommendation Framework Based on Probabilistic Relational Models, SSRN Electronic Journal, 2005.
DOI : 10.2139/ssrn.906513

V. Finn and . Jensen, An introduction to Bayesian networks, 1996.

J. Newton and R. Greiner, Hierarchical probabilistic relational models for collaborative filtering, Proc. Workshop on Statistical Relational Learning, 21st International Conference on Machine Learning, 2004.

J. Pearl, Reverend Bayes on inference engines: A distributed hierarchical approach, 1982.

J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, 1988.

F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Recommender Systems Handbook, p.9780387858197, 2010.

J. Stuart, P. Russell, and . Norvig, Artificial Intelligence: A Modern Approach, 2003.

B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, Application of dimensionality reduction in recommender system-a case study, 2000.

G. Shani and A. Gunawardana, Evaluating Recommendation Systems, Recommender systems handbook, pp.257-297, 2011.
DOI : 10.1007/978-0-387-85820-3_8

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

I. Tsamardinos, C. F. Aliferis, and A. Statnikov, Algorithms for large scale markov blanket discovery, Proceedings of the sixteenth international Florida artificial intelligence research society conference, pp.376-381, 2003.

I. Tsamardinos, L. E. Brown, and C. F. Aliferis, The max-min hill-climbing Bayesian network structure learning algorithm, Machine Learning, vol.9, issue.2/3, pp.31-78, 2006.
DOI : 10.1007/s10994-006-6889-7

L. Ungar, P. Dean, and . Foster, A formal statistical approach to collaborative filtering, CONALD'98, 1998.