R. P. Adams and G. E. Murray, Incorporating side information in probabilistic matrix factorization with gaussian processes, Proc. of UAI'10, 2010.

C. M. Bishop, Neural networks for pattern recognition, 1995.

C. Chen, D. Li, Y. Zhao, Q. Lv, and L. Shang, WEMAREC, Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '15
DOI : 10.1145/2766462.2767718

T. Chen, W. Zhang, Q. Lu, K. Chen, Z. Zheng et al., Svdfeature: a toolkit for feature-based collaborative filtering, JMLR, vol.13, issue.1, pp.3619-3622, 2012.

G. Dziugaite and D. Roy, Neural network matrix factorization. arXiv preprint, 2015.

X. Glorot and Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, Proc. of AISTATS'10, pp.249-256, 2010.

X. Glorot, A. Bordes, and Y. Bengio, Deep sparse rectifier neural networks, Proc. of AISTATS'11, pp.315-323, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00752497

C. Gomez-uribe and N. Hunt, The Netflix Recommender System, ACM Transactions on Management Information Systems, vol.6, issue.4, pp.1-1319, 2015.
DOI : 10.1145/2843948

Y. Kim and S. Choi, Scalable variational bayesian matrix factorization with side information, Proc. of AISTATS'14, 2014.

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

M. A. Kramer, Nonlinear principal component analysis using autoassociative neural networks, AIChE Journal, vol.37, issue.2, pp.233-243, 1991.
DOI : 10.1002/aic.690370209

R. Kumar, B. K. Verma, and S. S. Rastogi, Social Popularity based SVD++ Recommender System, International Journal of Computer Applications, vol.87, issue.14, p.2014
DOI : 10.5120/15279-4033

URL : http://doi.org/10.5120/15279-4033

Y. Lecun, Y. Bengio, and G. Hinton, Deep learning, Nature, vol.9, issue.7553, pp.436-444, 2015.
DOI : 10.1007/s10994-013-5335-x

Y. Lecun, L. Bottou, G. Orr, and K. Muller, Efficient backprop, Neural networks: Tricks of the trade, pp.9-48, 1998.

H. Lee, A. Battle, R. Raina, and A. Ng, Efficient sparse coding algorithms, Advances in neural information processing systems, pp.801-808, 2006.

J. Lee, S. Kim, G. Lebanon, and Y. Singerm, Local low-rank matrix approximation, Proc. of ICML'13, pp.82-90, 2013.

J. Lee, M. Sun, and G. Lebanon, A comparative study of collaborative filtering algorithms, 2012.

D. Li, C. Chen, Q. Lv, J. Yan, L. Shang et al., Low-rank matrix approximation with stability, Proc. of ICML'16, 2016.

S. Li, J. Kawale, and Y. Fu, Deep Collaborative Filtering via Marginalized Denoising Auto-encoder, Proceedings of the 24th ACM International on Conference on Information and Knowledge Management , CIKM '15, pp.811-820, 2015.
DOI : 10.1145/2806416.2806527

P. Lops, M. D. Gemmis, and G. Semeraro, Content-based Recommender Systems: State of the Art and Trends, Recommender systems handbook, pp.73-105, 2011.
DOI : 10.1007/978-0-387-85820-3_3

H. Ma, D. Zhou, C. Liu, M. R. Lyu, and I. King, Recommender systems with social regularization, Proceedings of the fourth ACM international conference on Web search and data mining, WSDM '11, pp.287-296, 2011.
DOI : 10.1145/1935826.1935877

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.352.9959

V. Miranda, J. Krstulovic, H. Keko, C. Moreira, and J. Pereira, Reconstructing Missing Data in State Estimation With Autoencoders, IEEE Transactions on Power Systems, vol.27, issue.2, pp.604-611, 2012.
DOI : 10.1109/TPWRS.2011.2174810

A. Mnih and R. Salakhutdinov, Probabilistic matrix factorization, NIPS'07, pp.1257-1264, 2007.

I. Porteous and M. W. Asuncion, Bayesian matrix factorization with side information and dirichlet process mixtures, Proc. of AAAI'10, 2010.

S. Rendle, Factorization Machines, 2010 IEEE International Conference on Data Mining, pp.995-1000, 2010.
DOI : 10.1109/ICDM.2010.127

R. Salakhutdinov and A. Mnih, Bayesian probabilistic matrix factorization using Markov chain Monte Carlo, Proceedings of the 25th international conference on Machine learning, ICML '08, pp.880-887, 2008.
DOI : 10.1145/1390156.1390267

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.5261

R. Salakhutdinov, A. Mnih, and G. Hinton, Restricted Boltzmann machines for collaborative filtering, Proceedings of the 24th international conference on Machine learning, ICML '07, pp.791-798, 2007.
DOI : 10.1145/1273496.1273596

S. Sedhain, A. K. Menon, S. Sanner, and L. Xie, AutoRec, Proceedings of the 24th International Conference on World Wide Web, WWW '15 Companion, pp.111-112, 2015.
DOI : 10.1145/2740908.2742726

N. Srivastava, G. Hinton, A. Krizhevsk, I. Sutskever, and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting, The Journal of Machine Learning Research, vol.15, issue.1, pp.1929-1958, 2014.

F. Strub and J. Mary, Collaborative Filtering with Stacked Denoising AutoEncoders and Sparse Inputs, NIPS Workshop on Machine Learning for eCommerce, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01256422

O. Teytaud, S. Gelly, and J. Mary, Active learning in regression, with application to stochastic dynamic programming, A. International Conference On Informatics in Control and Robotics ICINCO and CAP, pp.373-386, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00173204

T. T. Truyen, D. Phung, and S. Venkatesh, Ordinal boltzmann machines for collaborative filtering, Proc. of UAI'09, pp.548-556, 2009.

A. Van-den-oord, S. Dieleman, and B. Schrauwen, Deep content-based music recommendation, Proc. of NIPS'13, pp.2643-2651, 2013.

P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P. Manzagol, Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion, Jour. of Mach. Learn. Res, vol.11, issue.3, pp.3371-3408, 2010.

H. Wang, N. Wang, and D. Yeung, Collaborative Deep Learning for Recommender Systems, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '15, p.2014
DOI : 10.1145/2783258.2783273

X. Wang and Y. Wang, Improving Content-based and Hybrid Music Recommendation using Deep Learning, Proceedings of the ACM International Conference on Multimedia, MM '14, pp.627-636, 2014.
DOI : 10.1145/2647868.2654940

Y. Wu, C. Dubois, A. Zheng, and M. Ester, Collaborative Denoising Auto-Encoders for Top-N Recommender Systems, Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, WSDM '16, pp.153-162, 2016.
DOI : 10.1145/2835776.2835837

Y. Zhou, D. Wilkinson, R. Schreiber, and R. Pan, Large-Scale Parallel Collaborative Filtering for the Netflix Prize, Algorithmic Aspects in Information and Management, pp.337-348, 2008.
DOI : 10.1007/978-3-540-68880-8_32

F. Zhuang, D. Luo, X. Jin, H. Xiong, P. Luo et al., Representation Learning via Semi-Supervised Autoencoder for Multi-task Learning, 2015 IEEE International Conference on Data Mining, 2015.
DOI : 10.1109/ICDM.2015.22