. Abel, RecSys Challenge 2016, Proceedings of the 10th ACM Conference on Recommender Systems, RecSys '16, pp.425-426, 2016.
DOI : 10.1145/2645710.2645779

. Abel, RecSys Challenge 2017, Proceedings of the Eleventh ACM Conference on Recommender Systems , RecSys '17, pp.372-373, 2017.
DOI : 10.1016/j.cosrev.2016.05.002

. Abell, Features for Exploiting Black-Box Optimization Problem Structure, International Conference on Learning and Intelligent Optimization (LION7), pp.30-36, 2013.
DOI : 10.1007/978-3-642-44973-4_4

. Aggarwal, Horting hatches an egg, Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '99, pp.201-212, 1999.
DOI : 10.1145/312129.312230

. Ahalt, Competitive learning algorithms for vector quantization, Neural Networks, vol.3, issue.3, pp.277-290, 1990.
DOI : 10.1016/0893-6080(90)90071-R

A. Aizawa, An information-theoretic perspective of tf???idf measures, Information Processing & Management, vol.39, issue.1, pp.45-65, 2003.
DOI : 10.1016/S0306-4573(02)00021-3

. Amadini, Abstract, Theory and Practice of Logic Programming, vol.41, issue.4-5, pp.4-5509, 2014.
DOI : 10.1007/s10601-008-9051-2

. Andrews, Ioannis Tsochantaridis, and Thomas Hofmann. Support vector machines for multiple-instance learning, Advances in Neural Information Processing Systems (NIPS 2003), pp.577-584, 2003.

. Auger, Benchmarking of Continuous Black Box Optimization Algorithms, Evolutionary Computation, vol.20, issue.4, p.481, 2012.
DOI : 10.1162/EVCO_e_00091

URL : http://www.mitpressjournals.org/userimages/ContentEditor/1164817256746/lib_rec_form.pdf

S. Balabanovi´cbalabanovi´c, Y. Balabanovi´cbalabanovi´c, and . Shoham, Fab: content-based, collaborative recommendation, Communications of the ACM, vol.40, issue.3, pp.66-72, 1997.
DOI : 10.1145/245108.245124

K. Barkan, O. Barkan, and N. Koenigstein, ITEM2VEC: Neural item embedding for collaborative filtering, 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), pp.1-6, 2016.
DOI : 10.1109/MLSP.2016.7738886

URL : http://arxiv.org/pdf/1603.04259

, Nacim Belkhir. Per Instance Algorithm Configuration for Continuous Black Box Optimization, p.2017, 2017.

K. Bell, M. Robert, Y. Bell, and . Koren, Lessons from the Netflix prize challenge, ACM SIGKDD Explorations Newsletter, vol.9, issue.2, pp.75-79, 2007.
DOI : 10.1145/1345448.1345465

. Bengio, Neural Probabilistic Language Models, Journal of Machine Learning Research, vol.3, issue.Feb, pp.1137-1155, 2003.
DOI : 10.1007/3-540-33486-6_6

URL : https://hal.archives-ouvertes.fr/hal-01434258

. Bengio, Curriculum learning, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.41-48, 2009.
DOI : 10.1145/1553374.1553380

. Bennett, The netflix prize, Proceedings of KDD cup and workshop, p.35, 2007.

. Bird, Natural language processing with Python: analyzing text with the natural language toolkit, 2009.

. Bischl, ASlib: A benchmark library for algorithm selection, Artificial Intelligence, vol.237, pp.41-58, 2016.
DOI : 10.1016/j.artint.2016.04.003

M. Christopher and . Bishop, Pattern recognition and machine learning, 2006.

. Blei, Latent dirichlet allocation, Journal of Machine Learning Research, vol.3, pp.993-1022, 2003.

L. Blum, L. Avrim, P. Blum, and . Langley, Selection of relevant features and examples in machine learning, Artificial Intelligence, vol.97, issue.1-2, pp.245-271, 1997.
DOI : 10.1016/S0004-3702(97)00063-5

. Bojanowski, Enriching word vectors with subword information, Transactions of the Association for Computational Linguistics, vol.5, pp.135-146, 2017.

. Booker, A rigorous framework for optimization of expensive functions by surrogates. Structural optimization, pp.1-13, 1999.

. Branke, Finding Knees in Multi-objective Optimization, International Conference on Parallel Problem Solving from Nature (PPSN VIII), pp.722-731, 2004.
DOI : 10.1007/978-3-540-30217-9_73

]. Breiman, Random forests, Machine Learning, vol.45, issue.1, pp.5-32, 2001.
DOI : 10.1023/A:1010933404324

. Bromley, SIGNATURE VERIFICATION USING A ???SIAMESE??? TIME DELAY NEURAL NETWORK, International Journal of Pattern Recognition and Artificial Intelligence, vol.07, issue.04, pp.669-688, 1993.
DOI : 10.1142/S0218001493000339

. Burges, Learning to rank using gradient descent, Proceedings of the 22nd international conference on Machine learning , ICML '05, pp.89-96, 2005.
DOI : 10.1145/1102351.1102363

J. Christopher and . Burges, From RankNet to LambdaRank to LambdaMART: An overview. Learning, 2010.

. Burke, A Classification of Hyper-heuristic Approaches, Handbook of metaheuristics, pp.449-468, 2010.
DOI : 10.1007/978-1-4419-1665-5_15

. Büttcher, Information Retrieval: Implementing and Evaluating Search Engines, 2010.

. Cao, Learning to rank, Proceedings of the 24th international conference on Machine learning, ICML '07, pp.129-136, 2007.
DOI : 10.1145/1273496.1273513

. Carpi, Multi-stack ensemble for job recommendation, Proceedings of the Recommender Systems Challenge on, RecSys Challenge '16, p.8, 2016.
DOI : 10.1007/s10462-009-9124-7

. Cauwet, Algorithm portfolios for noisy optimization, Annals of Mathematics and Artificial Intelligence, vol.11, issue.4, pp.143-172, 2016.
DOI : 10.1007/978-3-642-31866-5_13

URL : https://hal.archives-ouvertes.fr/hal-01223113

. Cho, Learning Phrase Representations using RNN Encoder???Decoder for Statistical Machine Translation, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp.1724-1734, 2014.
DOI : 10.3115/v1/D14-1179

URL : https://hal.archives-ouvertes.fr/hal-01433235

. Chopra, Sumit Chopra, Raia Hadsell, and Yann LeCun Learning a similarity metric discriminatively, with application to face verification, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'2005), pp.539-546, 2005.

. Collautti, SNNAP: Solver-Based Nearest Neighbor for Algorithm Portfolios, Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML- PKDD'2013), pp.435-450, 2013.
DOI : 10.1007/978-3-642-40994-3_28

W. Collobert, J. Collobert, and . Weston, A unified architecture for natural language processing, Proceedings of the 25th international conference on Machine learning, ICML '08, pp.160-167, 2008.
DOI : 10.1145/1390156.1390177

C. Joseph and . Culberson, On the futility of blind search: An algorithmic view of " no free lunch, Evolutionary Computation, vol.6, issue.2, pp.109-127, 1998.

. Dagan, The PASCAL Recognising Textual Entailment Challenge, Machine learning challenges, pp.177-190, 2006.
DOI : 10.3115/1631862.1631868

. Das, Google news personalization, Proceedings of the 16th international conference on World Wide Web , WWW '07, pp.271-280, 2007.
DOI : 10.1145/1242572.1242610

, ACM, 2007.

, Indraneel Das. On characterizing the " knee " of the pareto curve based on normalboundary intersection, Structural Optimization, vol.18, issue.2-3, pp.107-115, 1999.

D. Kalyanmoy, Multi-objective evolutionary algorithms: Introducing bias among pareto-optimal solutions, Advances in evolutionary computing, pp.263-292, 2003.

. Springer, , 2003.

D. , G. Deb, and S. Gupta, Understanding knee points in bicriteria problems and their implications as preferred solution principles. Engineering optimization, pp.1175-1204, 2011.

. Deerwester, Indexing by latent semantic analysis, Journal of the American Society for Information Science, vol.41, issue.6, p.41391, 1990.
DOI : 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9

. Degroote, Reinforcement learning for automatic online algorithm selection-an empirical study, Proceedings of the 16th Conference Information Technologies-Applications and Theory (ITAT'2016), pp.93-101, 2016.

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

K. Desrosiers, G. Desrosiers, and . Karypis, A comprehensive survey of neighborhood-based recommendation methods. Recommender systems handbook, pp.107-144, 2011.

. Dietterich, Solving the multiple instance problem with axis-parallel rectangles, Artificial Intelligence, vol.89, issue.1-2, pp.31-71, 1997.
DOI : 10.1016/S0004-3702(96)00034-3

[. Santos, Gaussian Embeddings for Collaborative Filtering, Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval , SIGIR '17, 2017.
DOI : 10.1145/2939672.2939673

URL : https://hal.archives-ouvertes.fr/hal-01582488

. Droste, Perhaps not a free lunch but at least a free appetizer, Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation (GECCO), pp.833-839, 1999.

. Elkahky, Ali Mamdouh Elkahky, Yang Song, and Xiaodong He. A multi-view deep learning approach for cross domain user modeling in recommendation systems, Proceedings of the 24th International Conference on World Wide Web (WWW'2015) International World Wide Web Conferences Steering Committee, pp.278-288, 2015.

. Färber, An automated recommendation approach to selection in personnel recruitment, Americas Conference on Information Systems (AMCIS'2003) proceedings, p.302, 2003.

. Feurer, Efficient and robust automated machine learning, Advances in Neural Information Processing Systems (NIPS 2015), pp.2962-2970, 2015.

]. and A. Fisher, Design of Experiments, BMJ, vol.1, issue.3923, 1937.
DOI : 10.1136/bmj.1.3923.554-a

. Fouss, Random-Walk Computation of Similarities between Nodes of a Graph with Application to Collaborative Recommendation, IEEE Transactions on Knowledge and Data Engineering, vol.19, issue.3, pp.355-369, 2007.
DOI : 10.1109/TKDE.2007.46

M. Gagliolo and J. Schmidhuber, Learning dynamic algorithm portfolios, Annals of Mathematics and Artificial Intelligence, vol.18, issue.2, pp.295-328, 2006.
DOI : 10.1007/978-1-4757-2440-0

. Gantner, Learning Attribute-to-Feature Mappings for Cold-Start Recommendations, 2010 IEEE International Conference on Data Mining, pp.176-185, 2010.
DOI : 10.1109/ICDM.2010.129

. Gebser, A Portfolio Solver for Answer Set Programming: Preliminary Report, International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR'2011), pp.352-357, 2011.
DOI : 10.1007/s10601-008-9051-2

P. Gendreau, J. Gendreau, and . Potvin, Metaheuristics in Combinatorial Optimization, Annals of Operations Research, vol.1, issue.1, pp.189-213, 2005.
DOI : 10.1007/978-3-7091-6492-1_54

M. Gendreau and J. Potvin, Handbook of metaheuristics, 2010.
DOI : 10.1007/978-1-4419-1665-5

N. Georgiev, Kostadin Georgiev and Preslav Nakov. A non-iid framework for collaborative filtering with restricted boltzmann machines, Proceedings of the 30th Annual International Conference on Machine Learning (ICML'2013), pp.1148-1156, 2013.

X. Glorot and Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS), pp.249-256, 2010.

S. Gomes, P. Carla, B. Gomes, and . Selman, Algorithm portfolios, Artificial Intelligence, vol.126, issue.1-2, pp.43-62, 2001.
DOI : 10.1016/S0004-3702(00)00081-3

. Gomes, Boosting combinatorial search through randomization, Proceedings of the 15th National/10th Conference on Artificial Intelligence/Innovative Applications of Artificial Intelligence (AAAI/IAAI), pp.431-437, 1998.

. Gonard, François Gonard, Marc Schoenauer, and Michèle Sebag. Algorithm selector and prescheduler in the ICON challenge, International Conference on Metaheuristics and Nature Inspired Computing, 2016.

. Gonard, Asap.v2 and asap.v3: Sequential optimization of an algorithm selector and a scheduler, Proceedings of the Open Algorithm Selection Challenge, pp.8-11, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01659700

. Gopalan, Content-based recommendations with poisson factorization, Advances in Neural Information Processing Systems (NIPS 2014), pp.3176-3184, 2014.

P. Gori, A. Gori, and . Pucci, Itemrank: A random-walk based scoring algorithm for recommender engines, Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI-07), pp.2766-2771, 2007.

. Grbovic, E-commerce in Your Inbox, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '15, pp.1809-1818, 2015.
DOI : 10.1145/2623330.2623351

, ACM, 2015.

. Hadsell, Raia Hadsell, Sumit Chopra, and Yann LeCun. Dimensionality reduction by learning an invariant mapping, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.1735-1742, 2006.

. Hansen, Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES), Evolutionary Computation, vol.11, issue.1, pp.1-18, 2003.
DOI : 10.1162/106365601750190398

. Hansen, Coco: A platform for comparing continuous optimizers in a black-box setting. arXiv preprint, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01294124

. He, Neural Collaborative Filtering, Proceedings of the 26th International Conference on World Wide Web, WWW '17, pp.173-182, 2017.
DOI : 10.1145/2911451.2911502

, Verena Heidrich-Meisner and Christian Igel Hoeffding and bernstein races for selecting policies in evolutionary direct policy search, Proceedings of the 26th Annual International Conference on Machine Learning (ICML'2009), pp.401-408, 2009.

P. Hennig, J. Christian, and . Schuler, Entropy search for information-efficient global optimization, Journal of Machine Learning Research, vol.13, pp.1809-1837, 2012.

. Herlocker, An algorithmic framework for performing collaborative filtering, Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.230-237, 1999.

. Herlocker, Evaluating collaborative filtering recommender systems, ACM Transactions on Information Systems, vol.22, issue.1, pp.5-53, 2004.
DOI : 10.1145/963770.963772

S. Hinton, E. Geoffrey, . Hinton, R. Ruslan, and . Salakhutdinov, Reducing the Dimensionality of Data with Neural Networks, Science, vol.313, issue.5786, pp.313504-507, 2006.
DOI : 10.1126/science.1127647

A. Hoffer, Elad Hoffer and Nir Ailon Deep metric learning using triplet network, International Workshop on Similarity-Based Pattern Recognition, pp.84-92, 2015.

H. Holger and . Hoos, Programming by optimization, Communications of the ACM, vol.55, issue.2, pp.70-80, 2012.

. Hoos, Advances in algorithm selection for answer set programming, Theory and Practice of Logic Programming, vol.2, issue.14, pp.4-5569, 2014.

. Hoos, Abstract, Theory and Practice of Logic Programming, pp.117-142, 2015.
DOI : 10.1007/s10601-008-9061-0

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

. Huang, Learning deep structured semantic models for web search using clickthrough data, Proceedings of the 22nd ACM international conference on Conference on information & knowledge management, CIKM '13, pp.2333-2338, 2013.
DOI : 10.1145/2505515.2505665

. Huang, Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering, ACM Transactions on Information Systems, vol.22, issue.1, pp.116-142, 2004.
DOI : 10.1145/963770.963775

. Huberman, An Economics Approach to Hard Computational Problems, Science, vol.275, issue.5296, pp.27551-54, 1997.
DOI : 10.1126/science.275.5296.51

. Hutter, Sequential modelbased optimization for general algorithm configuration, International Conference on Learning and Intelligent Optimization (LION5), pp.507-523, 2011.

. Hutter, Identifying Key Algorithm Parameters and Instance Features Using Forward Selection, International Conference on Learning and Intelligent Optimization (LION7), pp.364-381, 2013.
DOI : 10.1007/978-3-642-44973-4_40

. Joachims, Accurately interpreting clickthrough data as implicit feedback, Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.154-161, 2005.

. Joachims, Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search, ACM Transactions on Information Systems, vol.25, issue.2, p.7, 2007.
DOI : 10.1145/1229179.1229181

. Kadioglu, ISAC-Instance-Specific Algorithm Configuration, European Conference on Artificial Intelligence (ECAI'2010), pp.751-756, 2010.

. Kadioglu, Algorithm Selection and Scheduling, International Conference on Principles and Practice of Constraint Programming (CP'2011), pp.454-469, 2011.
DOI : 10.1007/978-3-540-74970-7_50

. Kingma, . Ba, J. Kingma, and . Ba, Adam: A method for stochastic optimization . arXiv preprint arXiv:1412, 2014.

]. Koren, Factorization meets the neighborhood, Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD 08, pp.426-434, 2008.
DOI : 10.1145/1401890.1401944

B. Koren, . Koren, M. Robert, and . Bell, Advances in collaborative filtering, Recommender Systems Handbook, pp.77-118

. Koren, Matrix Factorization Techniques for Recommender Systems, Computer, vol.42, issue.8, p.42, 2009.
DOI : 10.1109/MC.2009.263

L. Kotthoff, Llama: leveraging learning to automatically manage algorithms. arXiv preprint, 2013.

L. Kotthoff, The ICON Challenge on Algorithm Selection, AI Magazine, vol.38, issue.2, 2015.
DOI : 10.1609/aimag.v38i2.2722

L. Kotthoff, Algorithm selection for combinatorial search problems: A survey, Data Mining and Constraint Programming: Foundations of a Cross-Disciplinary Approach, pp.149-190, 2016.

. Kotthoff, Auto-weka 2.0: Automatic model selection and hyperparameter optimization in weka, Journal of Machine Learning Research, vol.17, pp.1-5, 2016.

. Kotthoff, Open Algorithm Selection Challenge, 2017.

. Krizhevsky, ImageNet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems (NIPS 2012), pp.1097-1105, 2012.
DOI : 10.1162/neco.2009.10-08-881

. Kusner, From word embeddings to document distances, Proceedings of the 32nd Annual International Conference on Machine Learning (ICML'2015), pp.957-966, 2015.

. Kwok, . Tsang, T. James, I. W. Kwok, and . Tsang, Learning with idealized kernels, Proceedings of the 20th Annual International Conference on Machine Learning (ICML'2003), pp.400-407, 2003.

. Landauer, . Dumais, K. Thomas, . Landauer, T. Susan et al., A solution to Plato's problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge., Psychological Review, vol.104, issue.2, p.211, 1997.
DOI : 10.1037/0033-295X.104.2.211

M. Larochelle, I. Larochelle, and . Murray, The neural autoregressive distribution estimator, Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS), pp.29-37, 2011.

M. Le, V. Quoc, T. Le, and . Mikolov, Distributed representations of sentences and documents, Proceedings of the 31st Annual International Conference on Machine Learning (ICML'2014), pp.1188-1196, 2014.

L. Berre, S. , L. Berre, and L. Simon, The Essentials of the SAT 2003 Competition, International Conference on Theory and Applications of Satisfiability Testing (SAT'2003), pp.452-467, 2003.
DOI : 10.1007/978-3-540-24605-3_34

. Leyton-brown, Learning the Empirical Hardness of Optimization Problems: The Case of Combinatorial Auctions, International Conference on Principles and Practice of Constraint Programming, pp.556-572, 2002.
DOI : 10.1007/3-540-46135-3_37

. Leyton-brown, Jim Mc- Fadden, and Yoav Shoham. A portfolio approach to algorithm selection, Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI-03), pp.1542-1543, 2003.

. Lindauer, Autofolio: An automatically configured algorithm selector, Journal of Artificial Intelligence Research, vol.53, pp.745-778, 2015.
DOI : 10.24963/ijcai.2017/715

URL : https://www.ijcai.org/proceedings/2017/0715.pdf

. Lindauer, An Empirical Study of Per-instance Algorithm Scheduling, International Conference on Learning and Intelligent Optimization (LION10), pp.253-259, 2016.
DOI : 10.1016/S0065-2458(08)60520-3

. Lops, 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

. Loshchilov and . Hutter, Ilya Loshchilov and Frank Hutter. Online batch selection for faster training of neural networks, 2015.

. Ma, Abdullah Al-Dhelaan, Mznah Al-Rodhaan, and Sungyoung Lee. Social network and tag sources based augmenting collaborative recommender system, IEICE Transactions on Information and Systems, vol.98, issue.4, pp.902-910, 2015.

. Malherbe, Field selection for job categorization and recommendation to social network users, 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), pp.588-595, 2014.
DOI : 10.1109/ASONAM.2014.6921646

. Malinowski, Matching People and Jobs: A Bilateral Recommendation Approach, Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06), pp.137-137, 2006.
DOI : 10.1109/HICSS.2006.266

URL : http://csdl.computer.org/comp/proceedings/hicss/2006/2507/06/250760137c.pdf

. Malitsky, Algorithm portfolios based on cost-sensitive hierarchical clustering, Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI-13), pp.608-614, 2013.

. Maratea, A multi-engine approach to answer-set programming. Theory and Practice of Logic Programming, pp.841-868, 2014.

M. Mcnamee, J. Paul-mcnamee, and . Mayfield, Character N-Gram Tokenization for European Language Text Retrieval, Information Retrieval, vol.7, issue.1/2, pp.73-97, 2004.
DOI : 10.1023/B:INRT.0000009441.78971.be

. Mersmann, Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis, International Conference on Parallel Problem Solving from Nature (PPSN XI), pp.73-82, 2010.
DOI : 10.1007/978-3-642-15844-5_8

URL : http://coco.gforge.inria.fr/lib/exe/fetch.php?media=mersmann2010.pdf

. Mersmann, Exploratory landscape analysis, Proceedings of the 13th annual conference on Genetic and evolutionary computation, GECCO '11, pp.829-836, 2011.
DOI : 10.1145/2001576.2001690

. Michalewicz, Evolutionary algorithms for constrained engineering problems, Computers & Industrial Engineering, vol.30, issue.4, pp.851-870, 1996.
DOI : 10.1016/0360-8352(96)00037-X

URL : http://www.cs.cinvestav.mx/~constraint/papers/p30.ps.gz

. Mikolov, Efficient estimation of word representations in vector space, International Conference on Learning Representations Workshop, 2013.

. Mikolov, Distributed representations of words and phrases and their compositionality, Advances in Neural Information Processing Systems (NIPS 2013), pp.3111-3119, 2013.

, M?s?r and Sebag, 2017] Mustafa M?s?r and Michèle Sebag. Alors: An algorithm recommender system, Artificial Intelligence, vol.244, pp.291-314, 2017.

B. Morin, Frederic Morin and Yoshua Bengio Hierarchical probabilistic neural network language model, Proceedings of the 10th International Conference on Artificial Intelligence and Statistics (AISTATS), pp.246-252, 2005.

P. Mortensen, T. Dale, . Mortensen, A. Christopher, and . Pissarides, Job Creation and Job Destruction in the Theory of Unemployment, The Review of Economic Studies, vol.61, issue.3, pp.397-415, 1994.
DOI : 10.2307/2297896

T. Mueller, J. Mueller, and A. Thyagarajan, Siamese recurrent architectures for learning sentence similarity, Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI-16), pp.2786-2792, 2016.

. Muñoz, A metalearning prediction model of algorithm performance for continuous optimization problems, International Conference on Parallel Problem Solving from Nature (PPSN XII), pp.226-235, 2012.

. Muñoz, Instance spaces for machine learning classification, Machine Learning, 2017.

. Neculoiu, Learning Text Similarity with Siamese Recurrent Networks, Proceedings of the 1st Workshop on Representation Learning for NLP, p.148, 2016.
DOI : 10.18653/v1/W16-1617

Y. Andrew and . Ng, Feature selection, L1 vs L2 regularization and rotational invariance, Proceedings of the 21st Annual International Conference on Machine Learning, 2004.

B. Nguyen, V. Hieu, L. Nguyen, and . Bai, Cosine Similarity Metric Learning for Face Verification, Asian Conference on Computer Vision, pp.709-720, 2010.
DOI : 10.1007/11564386_26

. Nikoli´cnikoli´c, Mladen Nikoli´cNikoli´c, Filip Mari´cMari´c, and Predrag Jani?i´Jani?i´c. Simple algorithm portfolio for sat, Artificial Intelligence Review, pp.1-9, 2013.

. Nudelman, Yoav Shoham, and Holger Hoos. Satzilla: An algorithm portfolio for sat. Solver description, SAT competition, 2004.

. Nudelman, Understanding Random SAT: Beyond the Clauses-to-Variables Ratio, International Conference on Principles and Practice of Constraint Programming, pp.438-452, 2004.
DOI : 10.1007/978-3-540-30201-8_33

. Oentaryo, Algorithm selection via ranking, Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI-15), pp.1826-1832, 2015.

[. Mahony, Using case-based reasoning in an algorithm portfolio for constraint solving, Irish Conference on Artificial Intelligence and Cognitive Science, pp.210-216, 2008.

. Page, The pagerank citation ranking: Bringing order to the web, 1999.

. Pan, One-Class Collaborative Filtering, 2008 Eighth IEEE International Conference on Data Mining, pp.502-511, 2008.
DOI : 10.1109/ICDM.2008.16

. Papadopoulos, Inductive Confidence Machines for Regression, European Conference on Machine Learning, pp.345-356, 2002.
DOI : 10.1007/3-540-36755-1_29

. Paparrizos, Ioannis Paparrizos, B Barla Cambazoglu, and Aristides Gionis. Machine learned job recommendation, Proceedings of the 5th ACM Conference on Recommender Systems (RecSys'2011), pp.325-328, 2011.

K. Paquet, U. Paquet, and N. Koenigstein, One-class collaborative filtering with random graphs, Proceedings of the 22nd international conference on World Wide Web, WWW '13, pp.999-1008, 2013.
DOI : 10.1103/PhysRevE.72.036118

. Pazzani, . Billsus, J. Michael, D. Pazzani, and . Billsus, Content-Based Recommendation Systems, The adaptive web, pp.325-341, 2007.
DOI : 10.1007/978-3-540-72079-9_10

. Pedregosa, Scikit-learn: Machine learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

. Pennington, Glove: Global Vectors for Word Representation, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp.1532-1543, 2014.
DOI : 10.3115/v1/D14-1162

. Pfahringer, Tell me who can learn you and i can tell you who you are: Landmarking various learning algorithms, Proceedings of the 17th Annual International Conference on Machine Learning (ICML'2000), pp.743-750, 2000.

. Pizzato, A. Bhasin, A. Pizzato, and . Bhasin, Beyond friendship, Proceedings of the 7th ACM conference on Recommender systems, RecSys '13, pp.495-496, 2013.
DOI : 10.1145/2507157.2508064

M. David and . Powers, Applications and explanations of Zipf's law, Proceedings of the Joint Conferences on New Methods in Language Processing and Computational Natural Language Learning, pp.151-160, 1998.

G. Qamar, M. Ali, E. Qamar, and . Gaussier, RELIEF algorithm and similarity learning for k-NN, International Journal of Computer Information Systems and Industrial Management Applications (IJCISIM), vol.4, pp.445-458, 2012.

J. Radlinski, T. Radlinski, and . Joachims, Active exploration for learning rankings from clickthrough data, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '07, pp.570-579, 2007.
DOI : 10.1145/1281192.1281254

, ACM, 2007.

. Rashid, Getting to know you, Proceedings of the 7th international conference on Intelligent user interfaces , IUI '02, pp.127-134, 2002.
DOI : 10.1145/502716.502737

. Rendle, Bpr: Bayesian personalized ranking from implicit feedback, Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI'2009), pp.452-461, 2009.

R. John and . Rice, The algorithm selection problem, Advances in Computers, vol.15, pp.65-118, 1976.

. Ruotsalo, Interactive intent modeling, Communications of the ACM, vol.58, issue.1, pp.86-92, 2015.
DOI : 10.1561/1800000003

. Salakhutdinov, 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

, ACM, 2007.

. Sarwar, Analysis of recommendation algorithms for e-commerce, Proceedings of the 2nd ACM conference on Electronic commerce , EC '00, pp.158-167, 2000.
DOI : 10.1145/352871.352887

. Sarwar, Itembased collaborative filtering recommendation algorithms, Proceedings of the 10th International Conference on World Wide Web (WWW'2001), pp.285-295, 2001.

. Sayag, Tzur Sayag, Shai Fine, and Yishay Mansour. Combining multiple heuristics, STACS, pp.242-253, 2006.

G. Schafer, L. Joseph, . Schafer, W. John, and . Graham, Missing data: Our view of the state of the art., Psychological Methods, vol.7, issue.2, p.147, 2002.
DOI : 10.1037/1082-989X.7.2.147

. Schein, Methods and metrics for cold-start recommendations, Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval , SIGIR '02, pp.253-260, 2002.
DOI : 10.1145/564376.564421

. Schmitt, Matching jobs and resumes: a deep collaborative filtering task, 2nd Global Conference on Artificial Intelligence (GCAI'2016), pp.124-137, 2016.
DOI : 10.29007/17rz

URL : https://hal.archives-ouvertes.fr/hal-01378589

. Schmitt, Language Modelling for Collaborative Filtering: Application to Job Applicant Matching, 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), 2017.
DOI : 10.1109/ICTAI.2017.00186

URL : https://hal.archives-ouvertes.fr/hal-01659543

, IEEE, 2017.

. Sedhain, , 2015.

. Autorec, Autoencoders meet collaborative filtering, Proceedings of the 24th International Conference on World Wide Web (WWW'2015), pp.111-112, 2015.

. Shalev-shwartz, Online and batch learning of pseudo-metrics, Twenty-first international conference on Machine learning , ICML '04, p.94, 2004.
DOI : 10.1145/1015330.1015376

. Shi, CLiMF, Proceedings of the sixth ACM conference on Recommender systems, RecSys '12, pp.139-146, 2012.
DOI : 10.1145/2365952.2365981

A. Kate and . Smith-miles, Cross-disciplinary perspectives on meta-learning for algorithm selection, ACM Computing Surveys (CSUR), vol.41, issue.1, p.6, 2009.

A. Kate, L. Smith-miles, and . Lopes, Measuring instance difficulty for combinatorial optimization problems, Computers & Operations Research, vol.39, issue.5, pp.875-889, 2012.

T. Spielman, A. Daniel, S. Spielman, and . Teng, Smoothed analysis of algorithms, Journal of the ACM, vol.51, issue.3, pp.385-463, 2004.
DOI : 10.1145/990308.990310

. Srivastava, Dropout: a simple way to prevent neural networks from overfitting, Journal of Machine Learning Research, vol.15, issue.1, pp.1929-1958, 2014.

. Stern, Collaborative expert portfolio management, Proceedings of the 24th AAAI National Conference on Artificial Intelligence (AAAI-10), pp.179-184, 2010.

. Stern, Matchbox, Proceedings of the 18th international conference on World wide web, WWW '09, pp.111-120, 2009.
DOI : 10.1145/1526709.1526725

J. Matthew and . Streeter, Two broad classes of functions for which a no free lunch result does not hold, Proceedings of the 2003 Annual Conference on Genetic and Evolutionary Computation (GECCO), pp.1418-1430, 2003.

S. Streeter, J. Matthew, . Streeter, F. Stephen, and . Smith, New techniques for algorithm portfolio design, 2012.

H. Stützle, Thomas Stützle and Holger H Hoos. Max-min ant system and local search for the traveling salesman problem, IEEE International Conference on Evolutionary Computation (ICEC'97). Citeseer, 1997.

. Sun, MRLR: Multi-level Representation Learning for Personalized Ranking in Recommendation, Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017.
DOI : 10.24963/ijcai.2017/391

. Sutskever, Ilya Sutskever, Oriol Vinyals, and Quoc V Le. Sequence to sequence learning with neural networks, Advances in Neural Information Processing Systems (NIPS 2014), pp.3104-3112, 2014.

]. Talbi, Metaheuristics: From Design to Implementation, 2009.
DOI : 10.1002/9780470496916

URL : https://hal.archives-ouvertes.fr/hal-00750681

, Theano Development Team Theano: A Python framework for fast computation of mathematical expressions. arXiv e-prints, Theano Development Team, 2016.

. Thornton, Auto-WEKA, Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '13, pp.847-855, 2013.
DOI : 10.1145/2487575.2487629

, Laurens van der Maaten Learning a parametric embedding by preserving local structure, International Conference on Artificial Intelligence and Statistics (AISTATS), pp.384-391, 2009.

H. Van-der-maaten, . Laurens-van-der-maaten, E. Geoffrey, and . Hinton, Visualizing data using t-SNE, Journal of Machine Learning Research, vol.9, pp.2579-2605, 2008.

L. Van-der-maaten, Q. Kilian, and . Weinberger, Stochastic triplet embedding, 2012 IEEE International Workshop on Machine Learning for Signal Processing, pp.1-6, 2012.
DOI : 10.1109/MLSP.2012.6349720

, Koen Verstrepen Collaborative Filtering with Binary, Positive-only Data, p.2015, 2015.

G. Verstrepen, B. Verstrepen, and . Goethals, Unifying nearest neighbors collaborative filtering, Proceedings of the 8th ACM Conference on Recommender systems, RecSys '14, pp.177-184, 2014.
DOI : 10.1145/2645710.2645731

G. Verstrepen, B. Verstrepen, and . Goethals, Top-N Recommendation for Shared Accounts, Proceedings of the 9th ACM Conference on Recommender Systems, RecSys '15, pp.59-66, 2015.
DOI : 10.1145/1060745.1060754

B. Wang, . Wang, M. David, and . Blei, Collaborative topic modeling for recommending scientific articles, Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '11, pp.448-456, 2011.
DOI : 10.1145/2020408.2020480

. Wang, Learning Fine-Grained Image Similarity with Deep Ranking, 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp.1386-1393, 2014.
DOI : 10.1109/CVPR.2014.180

URL : https://authors.library.caltech.edu/61511/1/06909576.pdf

. Wang, Unifying user-based and item-based collaborative filtering approaches by similarity fusion, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval , SIGIR '06, pp.501-508, 2006.
DOI : 10.1145/1148170.1148257

. Weimer, Cofi rank-maximum margin matrix factorization for collaborative ranking, Advances in Neural Information Processing Systems, pp.1593-1600, 2008.

Q. Kilian, . Weinberger, K. Lawrence, and . Saul, Distance metric learning for large margin nearest neighbor classification, Journal of Machine Learning Research, vol.10, issue.Feb, pp.207-244, 2009.

. Weston, Latent collaborative retrieval, Proceedings of the 29th Annual International Conference on Machine Learning (ICML'2012), pp.443-450, 2012.

H. David and . Wolpert, The lack of a priori distinctions between learning algorithms, Neural computation, vol.8, issue.7, pp.1341-1390, 1996.

H. David and . Wolpert, The supervised learning no-free-lunch theorems, Soft computing and industry, pp.25-42, 2002.

H. David, . Wolpert, G. William, and . Macready, No free lunch theorems for optimization, IEEE Transactions on Evolutionary Computation, vol.1, issue.1, pp.67-82, 1997.

. Wu, Sampling Matters in Deep Embedding Learning, 2017 IEEE International Conference on Computer Vision (ICCV), 2017.
DOI : 10.1109/ICCV.2017.309

URL : http://arxiv.org/pdf/1706.07567

. Wu, 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 : 10.1145/2043932.2043940

. Xing, Distance metric learning with application to clustering with side-information, Advances in Neural Information Processing Systems (NIPS 2003), pp.521-528, 2003.

. Xu, SATzilla-07: The Design and Analysis of an Algorithm Portfolio for SAT, Principles and Practice of Constraint Programming (CP'2007), pp.712-727, 2007.
DOI : 10.1007/978-3-540-74970-7_50

. Xu, Satzilla: portfolio-based algorithm selection for sat, Journal of Artificial Intelligence Research, pp.565-606, 2008.

. Xu, Evaluating Component Solver Contributions to Portfolio-Based Algorithm Selectors, International Conference on Theory and Applications of Satisfiability Testing (SAT'2012), pp.228-241, 2012.
DOI : 10.1007/978-3-642-31612-8_18

URL : http://www.cs.ubc.ca/%7Ehoos/Publ/XuEtAl12.pdf

. Xu, Satzilla2012: Improved algorithm selection based on cost-sensitive classification models, Proceedings of the 2012 SAT Challenge, pp.57-58, 2012.

Y. , An efficient algorithm for local distance metric learning, Proceedings of the 21st National/18th Conference on Artificial Intelligence/Innovative Applications of Artificial Intelligence (AAAI/IAAI), pp.543-548, 2006.

. Zheng, A neural autoregressive approach to collaborative filtering, Proceedings of the 33th Annual International Conference on Machine Learning (ICML'2016), pp.764-773, 2016.

. Zhou, Bipartite network projection and personal recommendation, Physical Review E, vol.22, issue.4, p.46115, 2007.
DOI : 10.1023/A:1011419012209

. Zhu, Learning cross-domain neural networks for sketch-based 3d shape retrieval, Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI-16), pp.3683-3689, 2016.