R. Bell, Y. Koren, and C. Volinsky, Modeling relationships at multiple scales to improve accuracy of large recommender systems, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '07, pp.95-104, 2007.
DOI : 10.1145/1281192.1281206

J. Bennett and S. Lanning, The netflix prize, KDD Cup and Workshop, 2007.

I. Borg and P. J. Groenen, Modern Multidimensional Scaling: Theory and Applications, Journal of Educational Measurement, vol.40, issue.3, 2005.
DOI : 10.1007/BF02289341

S. Bostandjiev, J. O. Donovan, and T. Höllerer, TasteWeights, Proceedings of the sixth ACM conference on Recommender systems, RecSys '12, pp.35-42, 2012.
DOI : 10.1145/2365952.2365964

S. Chen, J. L. Moore, D. Turnbull, and T. Joachims, Playlist prediction via metric embedding, Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '12, pp.714-722, 2012.
DOI : 10.1145/2339530.2339643
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.639.8768

V. Chen, C. Qian, and R. Woodbury, Visualizing Collaborative Filtering in Digital Collections, 2007 11th International Conference Information Visualization (IV '07), pp.203-210, 2007.
DOI : 10.1109/IV.2007.133

T. F. Cox and M. Cox, Multidimensional Scaling, Second Edition, 2000.
DOI : 10.1201/9781420036121

P. Demartines and J. Herault, Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets, IEEE Transactions on Neural Networks, vol.8, issue.1, pp.148-154, 1997.
DOI : 10.1109/72.554199

M. Deshpande and G. Karypis, recommendation algorithms, ACM Transactions on Information Systems, vol.22, issue.1, pp.143-177, 2004.
DOI : 10.1145/963770.963776

P. Drineas, I. Kerenidis, and P. Raghavan, Competitive recommendation systems, Proceedings of the thiry-fourth annual ACM symposium on Theory of computing , STOC '02, pp.82-90, 2002.
DOI : 10.1145/509907.509922

P. Eades, A heuristic for graph drawing, Congressus Numerantium, pp.149-160, 1984.

T. M. Fruchterman and E. M. Reingold, Graph drawing by forcedirected placement, Software-Practice and Experience, pp.1129-1164, 1991.

E. Gansner, Y. Hu, S. Kobourov, and C. Volinsky, Putting recommendations on the map, Proceedings of the third ACM conference on Recommender systems, RecSys '09, pp.345-348, 2009.
DOI : 10.1145/1639714.1639784

E. R. Gansner, Y. Hu, and S. North, A maxent-stress model for graph layout, IEEE Transactions on Visualization and Computer Graphics, pp.927-940, 2013.

U. O. Grouplens, MovieLens Datasets, 2010.

J. L. Herlocker, J. A. Konstan, A. Borchers, and J. , An algorithmic framework for performing collaborative filtering, Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval , SIGIR '99, pp.230-237, 1999.
DOI : 10.1145/312624.312682

T. Hofmann, Latent semantic models for collaborative filtering, ACM Transactions on Information Systems, vol.22, issue.1, pp.89-115, 2004.
DOI : 10.1145/963770.963774

A. Kermarrec and A. Moin, FlexGD: A flexible force-directed model for graph drawing, 2013 IEEE Pacific Visualization Symposium (PacificVis), pp.217-224, 2013.
DOI : 10.1109/PacificVis.2013.6596148
URL : https://hal.archives-ouvertes.fr/hal-00679574

M. Khoshneshin and W. N. Street, Collaborative filtering via euclidean embedding, Proceedings of the fourth ACM conference on Recommender systems, RecSys '10, 2010.
DOI : 10.1145/1864708.1864728
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.308.7304

J. Z. Kolter and M. A. Maloof, Dynamic weighted majority: An ensemble method for drifting concepts, J. Mach. Learn. Res, pp.2755-2790, 2007.

Y. Koren, Collaborative filtering with temporal dynamics, Proc. of the 15th ACM SIGKDD, pp.447-456, 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

G. Linden, B. Smith, and J. York, Amazon.com recommendations: item-to-item collaborative filtering, Internet Computing, pp.76-80, 2003.
DOI : 10.1109/MIC.2003.1167344

R. M. Bell and Y. Koren, Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights, Seventh IEEE International Conference on Data Mining (ICDM 2007), pp.43-52, 2007.
DOI : 10.1109/ICDM.2007.90

R. Salakhutdinov and A. Mnih, Probabilistic matrix factorization, Advances in Neural Information Processing Systems, 2007.

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

G. Widmer and M. Kubat, Learning in the presence of concept drift and hidden contexts, Machine Learning, vol.27, issue.11, pp.69-101, 1996.
DOI : 10.1007/BF00116900

R. Xiong, M. A. Smith, and S. M. Drucker, Visualizations of collaborative information for end-users, 1999.