, Learning to Recommend Diverse Items over Implicit Feedback on PANDOR with Charlotte Laclau, 2018.

, Health Monitoring on Social Media over Time with Sihem Amer-Yahia, Marianne Clausel, vol.30, pp.1467-1480, 2018.

, KASANDR: A Large-Scale Dataset with Implicit Feedback for Recommendation with Charlotte Laclau, 2017.

, Submitted articles 5. Representation Learning and Pairwise Ranking for Implicit Feedback in Recommendation Systems with Mikhail Trofimov, Marianne Clausel, Massih-Reza Amini, SIGIR 2016

H. Abdollahpouri, R. Burke, and B. Mobasher, Controlling popularity bias in learning-to-rank recommendation, Proceedings of RecSys, vol.6, p.76, 2017.

N. Ailon and M. Mohri, An efficient reduction of ranking to classification, Proceedings of COLT, p.75, 2008.

R. Massih, N. Amini, and . Usunier, Learning with Partially Labeled and Interdependent Data, pp.978-981, 2015.

C. Anderson, The Long Tail: Why the Future of Business Is Selling Less of More. Hyperion, p.48, 2006.

C. Andrieu, A. Nando-de-freitas, M. I. Doucet, and . Jordan, An introduction to MCMC for machine learning, Machine Learning, vol.50, pp.5-43, 2003.

M. Balabanovic and Y. Shoham, Content-based, collaborative recommendation

, Commun. ACM, vol.40, issue.3, p.105, 1997.

S. Balakrishnan and S. Chopra, Collaborative ranking, WSDM, 2012.

T. Bansal, D. Belanger, and A. Mccallum, Ask the GRU: Multi-task learning for deep text recommendations, Proceedings of the 10th ACM Conference on Recommender Systems, p.24, 2016.

O. Barkan and N. Koenigstein, ITEM2VEC: neural item embedding for collaborative filtering, International Workshop on Machine Learning for Signal Processing, MLSP, vol.77, p.96, 2016.

O. Barkan and N. Koenigstein, Item2vec: Neural item embedding for collaborative filtering, Proceedings of the Poster Track of RecSys, p.27, 2016.

J. Basilico and Y. Raimond, Déjà vu: The importance of time and causality in recommender systems, Proceedings of RecSys, vol.31, p.50, 2017.

C. Basu, H. Hirsh, and W. W. Cohen, Recommendation as classification: Using social and content-based information in recommendation, AAAI/IAAI, p.105, 1998.

Y. Bengio, R. Ducharme, P. Vincent, and C. Janvin, A neural probabilistic language model, Journal of Machine Learning Research, vol.3, pp.1137-1155, 2003.

D. M. Blei, A. Y. Ng, and M. I. Jordan, Latent Dirichlet Allocation, Journal of Machine Learning, vol.3, p.52, 2003.

R. Boim, T. Milo, and S. Novgorodov, Diversification and refinement in collaborative filtering recommender, Proceedings of CIKM, p.28, 2011.

L. Bottou, Stochastic gradient descent tricks, Neural Networks: Tricks of the Trade -Second Edition, p.74, 2012.

K. Bradley and B. Smyth, Improving recommendation diversity, vol.6, p.28, 2001.

J. C. Christopher and . Burges, From RankNet to LambdaRank to LambdaMART: An overview, p.23, 2010.

J. C. Christopher, T. Burges, E. Shaked, A. Renshaw, M. Lazier et al., Learning to rank using gradient descent, Proceedings of ICML, p.24, 2005.

P. G. Campos, F. Díez, and I. Cantador, Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols, User Model. User-Adapt. Interact, vol.24, issue.1-2, p.50, 2014.

J. G. Carbonell and J. Goldstein, The use of mmr, diversity-based reranking for reordering documents and producing summaries, Proceedings of SIGIR, p.28, 1998.

R. Caruana, S. Baluja, and T. M. Mitchell, Using the future to sort out the present: Rankprop and multitask learning for medical risk evaluation, Proceedings of NIPS, p.24, 1995.

P. Castells, N. J. Hurley, and S. Vargas, Novelty and diversity in recommender systems, Recommender Systems Handbook, p.34, 2015.

S. Chatzis, P. Christodoulou, and A. S. Andreou, Recurrent latent variable networks for session-based recommendation. CoRR, abs/1706.04026, vol.24, p.50, 2017.

W. Chen, T. Liu, Y. Lan, Z. Ma, and H. Li, Ranking measures and loss functions in learning to rank, Proceedings of NIPS, p.22, 2009.

P. Cheng, S. Wang, J. Ma, J. Sun, and H. Xiong, Learning to recommend accurate and diverse items, Proceedings of the 26th International Conference on World Wide Web, WWW '17, p.28, 2017.

M. Soudip-roy-chowdhury, . Imran, S. Muhammad-rizwan-asghar, C. Amer-yahia, and . Castillo, Tweet4act: Using incident-specific profiles for classifying crisis-related messages, 10th Proceedings of the International Conference on Information Systems for Crisis Response and Management, p.58, 2013.

W. Chu and S. Park, Personalized recommendation on dynamic content using predictive bilinear models, Proceedings of WWW, p.105, 2009.

M. Claypool, A. Gokhale, T. Miranda, P. Murnikov, D. Netes et al., Combining content-based and collaborative filters in an online newspaper, p.105, 1999.

W. W. Cohen, R. E. Schapire, and Y. Singer, Learning to order things

, J. Artif. Intell. Res. (JAIR), vol.10, pp.243-270, 1921.

C. Cortes and V. Vapnik, Support-vector networks, Machine Learning, vol.20, pp.273-297, 1995.

P. Covington, J. Adams, and E. Sargin, Deep neural networks for youtube recommendations, Proceedings of the 10th ACM Conference on Recommender Systems, RecSys '16, p.24, 2016.

P. Covington, J. Adams, and E. Sargin, Deep neural networks for youtube recommendations, Proceedings of RecSys, p.27, 2016.

K. Crammer and Y. Singer, Pranking with ranking, Proceedings of NIPS, p.20, 2001.

H. Dai, Y. Wang, R. Trivedi, and L. Song, Recurrent coevolutionary feature embedding processes for recommendation, p.24, 2016.

T. Davidson, D. Warmsley, M. W. Macy, and I. Weber, Automated hate speech detection and the problem of offensive language, Proceedings of the Eleventh International Conference on Web and Social Media, ICWSM 2017, p.58, 2017.

T. Deselaers, T. Gass, P. Dreuw, and H. Ney, Jointly optimising relevance and diversity in image retrieval, Proceedings of CIVR, p.28, 2009.

M. Deshpande and G. Karypis, Item-based top-N recommendation algorithms, ACM Trans. Inf. Syst, vol.22, issue.1, pp.143-177, 2004.

Y. Ding and X. Li, Time weight collaborative filtering, Proceedings of the 14th ACM International Conference on Information and Knowledge Management, CIKM '05, p.50, 2005.

M. Drosou and E. Pitoura, Diversity over continuous data, IEEE Data Eng. Bull, vol.32, issue.4, p.28, 2009.

A. Mamdouh-elkahky, Y. Song, and X. He, A multi-view deep learning approach for cross domain user modeling in recommendation systems, Proceedings of WWW, p.24, 2015.

Y. Freund, R. D. Iyer, R. E. Schapire, and Y. Singer, An efficient boosting algorithm for combining preferences, Journal of Machine Learning Research, vol.4, pp.933-969, 2003.

S. Funk, Netflix update: Try this at home, p.15, 2006.

A. Gilotte, C. Calauzènes, T. Nedelec, A. Abraham, and S. Dollé, Offline A/B testing for recommender systems, Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, WSDM 5-9, p.35, 2018.
DOI : 10.1145/3159652.3159687
URL : http://arxiv.org/pdf/1801.07030

N. Good, J. B. Schafer, J. A. Konstan, A. Borchers, M. Badrul et al., Combining collaborative filtering with personal agents for better recommendations, AAAI/IAAI, p.105, 1999.

M. Grbovic, V. Radosavljevic, N. Djuric, N. Bhamidipati, J. Savla et al., E-commerce in your inbox: Product recommendations at scale, Proceedings of SIGKDD, p.27, 2015.
DOI : 10.1145/2783258.2788627
URL : http://arxiv.org/pdf/1606.07154

M. Grbovic, V. Radosavljevic, N. Djuric, N. Bhamidipati, J. Savla et al., E-commerce in your inbox: Product recommendations at scale. CoRR, abs/1606.07154, 2016. 25, 27 REFERENCES REFERENCESÉlie REFERENCES´REFERENCESÉlieGù ardia-Sebaoun, Vincent Guigue, and Patrick Gallinari. Latent trajectory modeling: A light and efficient way to introduce time in recommender systems, Proceedings of RecSys, p.27, 2015.

F. Guillou, On Recommendation Systems in a Sequential Context. (Des Systèmes de Recommandation dans un Contexte Séquentiel), vol.24, p.30, 2016.
URL : https://hal.archives-ouvertes.fr/tel-01502499

A. Gunawardana and G. Shani, Evaluating recommender systems, Recommender Systems Handbook, vol.30, p.35, 2015.

F. , M. Harper, and J. A. Konstan, The movielens datasets: History and context, ACM Trans. Interact. Intell. Syst, vol.5, issue.4, p.83, 2015.
DOI : 10.1145/2827872

R. He and J. Mcauley, VBPR: visual bayesian personalized ranking from implicit feedback, p.24, 2015.

R. He and J. Mcauley, VBPR: visual bayesian personalized ranking from implicit feedback, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, p.24, 2016.

X. He, H. Zhang, M. Kan, and T. Chua, Fast matrix factorization for online recommendation with implicit feedback, Proceedings of SIGIR, pp.549-558, 2016.
DOI : 10.1145/2911451.2911489
URL : http://arxiv.org/pdf/1708.05024

X. He, L. Liao, H. Zhang, L. Nie, X. Hu et al., Neural collaborative filtering, Proceedings of WWW, p.27, 2017.

J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl, An algorithmic framework for performing collaborative filtering, SIGIR '99: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, p.12, 1999.
DOI : 10.1145/3130348.3130372

J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. Riedl, Evaluating collaborative filtering recommender systems, ACM Trans. Inf. Syst, vol.22, issue.1, p.28, 2004.
DOI : 10.1145/963770.963772

B. Hidasi and A. Karatzoglou, Recurrent neural networks with top-k gains for session-based recommendations. CoRR, abs/1706.03847, vol.24, p.50, 2017.
DOI : 10.1145/3269206.3271761
URL : http://arxiv.org/pdf/1706.03847

B. Hidasi and A. Karatzoglou, Linas Baltrunas, and Domonkos Tikk. Session-based recommendations with recurrent neural networks, vol.24, p.50, 2015.

B. Hidasi, M. Quadrana, A. Karatzoglou, and D. Tikk, Parallel recurrent neural network architectures for feature-rich session-based recommendations, Proceedings of the 10th ACM Conference on Recommender Systems, vol.24, p.50, 2016.
DOI : 10.1145/2959100.2959167

Y. Hu, Y. Koren, and C. Volinsky, Collaborative filtering for implicit feedback datasets, Proceedings of ICDM, vol.16, p.81, 2008.

Y. Hu, Y. Koren, and C. Volinsky, Collaborative filtering for implicit feedback datasets, Proceedings of ICDM, p.81, 2008.

Y. Hu, Y. Koren, and C. Volinsky, Collaborative filtering for implicit feedback datasets, Proceedings ICDM, p.17, 2008.
DOI : 10.1109/icdm.2008.22

N. J. Hurley, Personalised ranking with diversity, Proceedings of RecSys, p.29, 2013.
DOI : 10.1145/2507157.2507226

M. Jahrer and A. Tscher, Collaborative filtering ensemble for ranking, KDD Cup, vol.18, pp.153-167, 2012.

P. Jain, P. Netrapalli, and S. Sanghavi, Low-rank matrix completion using alternating minimization, Symposium on Theory of Computing Conference, pp.665-674, 2013.
DOI : 10.1145/2488608.2488693
URL : http://arxiv.org/abs/1212.0467

S. Janson, Large Deviations for Sums of Partly Dependent Random Variables, Random Structures and Algorithms, vol.24, issue.3, p.67, 2004.

T. Joachims, Optimizing search engines using clickthrough data, Proceedings of SIGKDD, pp.133-142, 1921.
DOI : 10.1145/775066.775067

T. Joachims and A. Swaminathan, Counterfactual evaluation and learning for search, recommendation and ad placement, Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, SIGIR, p.35, 2016.

Y. Juan, Y. Zhuang, W. Chin, and C. Lin, Field-aware factorization machines for CTR prediction, Proceedings of RecSys, p.81, 2016.

Y. Juan, Y. Zhuang, W. Chin, and C. Lin, Field-aware factorization machines for CTR prediction, Proceedings of the 10th ACM Conference on Recommender Systems, p.19, 2016.

Y. Juan, D. Lefortier, and O. Chapelle, Field-aware factorization machines in a real-world online advertising system, Proceedings of WWW, pp.680-688, 2017.

T. Kamba, K. Bharat, and M. C. Albers, The krakatoa chronicle: An interactive personalized newspaper on the web, World Wide Web Journal, vol.1, issue.1

P. Diederik, J. Kingma, and . Ba, Adam: A method for stochastic optimization

. Corr, , vol.87, 2014.

R. Kohavi, Online controlled experiments: Lessons from running a/b/n tests for 12 years, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '15, p.35, 2015.

Y. Koren, Factorization meets the neighborhood: a multifaceted collaborative filtering model, Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol.16, p.17, 2008.

Y. Koren, Collaborative filtering with temporal dynamics, Commun. ACM, vol.53, issue.4, p.50, 2010.
DOI : 10.1145/1557019.1557072

Y. Koren, R. M. Bell, and C. Volinsky, Matrix factorization techniques for recommender systems, IEEE Computer, vol.42, issue.8, p.81, 2009.
DOI : 10.1109/mc.2009.263

M. Kula, Metadata embeddings for user and item cold-start recommendations, Proceedings of the 2nd Workshop on New Trends on Content-Based Recommender Systems co-located with RecSys, vol.81, p.87, 2015.

J. Lee, S. Bengio, S. Kim, G. Lebanon, and Y. Singer, Local collaborative ranking, Proceedings of the 23rd International Conference on World Wide Web, WWW '14, p.24, 2014.
DOI : 10.1145/2566486.2567970

E. L. Lehmann and H. J. D'abrera, Nonparametrics: statistical methods based on ranks, p.88, 2006.

O. Levy and Y. Goldberg, Neural word embedding as implicit matrix factorization, Proceedings of NIPS, p.26, 2014.

P. Li, J. C. Christopher, Q. Burges, and . Wu, Mcrank: Learning to rank using multiple classification and gradient boosting, Proceedings of NIPS, p.20, 2007.

X. Li and T. Murata, Using multidimensional clustering based collaborative filtering approach improving recommendation diversity, 2012.
DOI : 10.1109/wi-iat.2012.229

. Ieee/wic/, ACM International Conferences on Web Intelligence and Intelligent Agent Technology, p.28, 2012.

D. Liang, J. Altosaar, L. Charlin, and D. M. Blei, Factorization meets the item embedding: Regularizing matrix factorization with item co-occurrence, Proceedings of RecSys, vol.27, p.81, 2016.
DOI : 10.1145/2959100.2959182

T. Liu, Learning to rank for information retrieval, Foundations and Trends in Information Retrieval, vol.3, issue.3, p.20, 2009.

X. Liu and K. Aberer, Towards a dynamic top-n recommendation framework, Eighth ACM Conference on Recommender Systems, RecSys '14, p.24, 2014.

P. Lops, G. Marco-de-gemmis, and . Semeraro, Content-based recommender systems: State of the art and trends, Recommender Systems Handbook, p.11, 2011.

W. Machmouchi and G. Buscher, Principles for the design of online A/B metrics, Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, SIGIR 2016, p.35, 2016.

L. Manikonda and M. Choudhury, Modeling and understanding visual attributes of mental health disclosures in social media, Proceedings of the 2017

, CHI Conference on Human Factors in Computing Systems, p.58, 2017.

J. J. Mcauley, C. Targett, Q. Shi, A. Van-den, and . Hengel, Image-based recommendations on styles and substitutes, Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, p.24, 2015.

C. Mcdiarmid, On the method of bounded differences, Survey in Combinatorics, p.67, 1989.

S. M. Mcnee, J. Riedl, and J. A. Konstan, Being accurate is not enough: how accuracy metrics have hurt recommender systems, Extended Abstracts Proceedings of the Conference on Human Factors in Computing Systems, CHI, vol.6, p.28, 2006.

D. Mcsherry, Diversity-conscious retrieval, Advances in Case-Based Reasoning, 6th European Conference, ECCBR, vol.6, p.28, 2002.

T. Mikolov, K. Chen, G. Corrado, and J. Dean, Efficient estimation of word representations in vector space. CoRR, abs/1301, vol.3781, 2013.

T. Mikolov, K. Chen, G. Corrado, and J. Dean, Efficient estimation of word representations in vector space, p.26, 2013.

T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, Distributed representations of words and phrases and their compositionality, Proceedings of NIPS, p.26, 2013.

T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, Distributed representations of words and phrases and their compositionality, Proceedings of NIPS, p.26, 2013.

J. Raymond, L. Mooney, and . Roy, Content-based book recommending using learning for text categorization. CoRR, cs.DL/9902011

C. Musto, G. Semeraro, P. Marco-de-gemmis, and . Lops, Learning word embeddings from wikipedia for content-based recommender systems, Proceedings of ECIR, vol.9626, p.50, 2016.

C. Musto, G. Marco-de-gemmis, P. Semeraro, and . Lops, A multicriteria recommender system exploiting aspect-based sentiment analysis of users' reviews, Proceedings of RecSys, RecSys '17, p.50, 2017.

T. Nedelec, E. Smirnova, and F. Vasile, Specializing joint representations for the task of product recommendation, Proceedings of the 2nd Workshop on Deep Learning for Recommender Systems, vol.25, p.27, 2017.

S. Park, D. M. Pennock, O. Madani, N. Good, and D. Decoste, Na¨?veNa¨?ve filterbots for robust cold-start recommendations, Proceedings of SIGKDD, p.105, 2006.

Y. Park and A. Tuzhilin, The long tail of recommender systems and how to leverage it, Proceedings of RecSys, p.48, 2008.

A. Paterek, Improving regularized singular value decomposition for collaborative filtering, p.16, 2007.

B. Paudel, T. Haas, and A. Bernstein, Fewer flops at the top: Accuracy, diversity, and regularization in two-class collaborative filtering, Proceedings of RecSys, pp.1-6, 2017.

J. Michael, M. Paul, and . Dredze, You Are What You Tweet: Analyzing Twitter for Public Health, ICWSM'11, p.61, 2011.

J. Michael, R. Paul, and . Girju, A two-dimensional topic-aspect model for discovering multi-faceted topics, AAAI, vol.53, p.57, 2010.

M. J. Pazzani, A framework for collaborative, content-based and demographic filtering, Artificial Intelligence Review, p.105, 1999.

J. Michael, D. Pazzani, and . Billsus, Content-based recommendation systems, The Adaptive Web, vol.4321, p.11, 2007.

J. Pennington, F. X. Yu, and S. Kumar, Spherical random features for polynomial kernels, Proceedings of NIPS, p.26, 2015.

J. Pessiot, T. Truong, N. Usunier, M. Amini, and P. Gallinari, Learning to rank for collaborative filtering, Proceedings of ICEIS, p.21, 2007.
URL : https://hal.archives-ouvertes.fr/hal-01336029

Y. Qu, H. Cai, K. Ren, W. Zhang, Y. Yu et al., Product-based neural networks for user response prediction, IEEE 16th International Conference on Data Mining, ICDM 2016, p.24, 2016.

M. Quadrana, A. Karatzoglou, B. Hidasi, and P. Cremonesi, Personalizing session-based recommendations with hierarchical recurrent neural networks, Proceedings of the Eleventh ACM Conference on Recommender Systems, RecSys, vol.24, p.50, 2017.

L. Ralaivola and M. Amini, Proceedings of ICML, vol.68, p.69, 2015.

S. Rendle, Factorization machines, Proceedings of ICDM, vol.18, p.81, 2010.

S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-thieme, BPR: bayesian personalized ranking from implicit feedback, Proceedings of UAI, vol.22, p.81, 2009.

S. Rendle, C. Freudenthaler, and L. Schmidt-thieme, Factorizing personalized markov chains for next-basket recommendation, Proceedings of the 19th International Conference on World Wide Web, WWW 2010, p.50, 2010.
DOI : 10.1145/1772690.1772773
URL : http://www.ismll.uni-hildesheim.de/pub/pdfs/RendleFreudenthaler2010-FPMC.pdf

Z. Steffen-rendle, C. Gantner, L. Freudenthaler, and . Schmidt-thieme, Fast context-aware recommendations with factorization machines, Proceedings of SIGIR, pp.635-644, 2011.

A. Marco-túlio-ribeiro, A. Lacerda, N. Veloso, and . Ziviani, Paretoefficient hybridization for multi-objective recommender systems, Proceedings of RecSys, p.29, 2012.

L. Rigutini, T. Papini, M. Maggini, and M. Bianchini, A neural network approach for learning object ranking, Proceedings of ICANN, p.24, 2008.
DOI : 10.1007/978-3-540-87559-8_93

L. Rigutini, T. Papini, M. Maggini, and F. Scarselli, Sortnet: Learning to rank by a neural preference function, IEEE Trans. Neural Networks, vol.22, issue.9, p.24, 2011.
DOI : 10.1109/tnn.2011.2160875

M. Ruocco, O. Steinar-lillestøl, H. Skrede, and . Langseth, Inter-session modeling for session-based recommendation, Proceedings of the 2Nd Workshop on Deep Learning for Recommender Systems, vol.24, p.50, 2017.
DOI : 10.1145/3125486.3125491
URL : http://arxiv.org/pdf/1706.07506

R. Salakhutdinov, A. Mnih, and G. E. Hinton, Restricted boltzmann machines for collaborative filtering, Proceedings of ICML, p.24, 2007.
DOI : 10.1145/1273496.1273596

M. Badrul, G. Sarwar, J. A. Karypis, J. T. Konstan, and . Riedl, Application of dimensionality reduction in recommender system -a case study, ACM WEBKDD WORKSHOP, p.15, 2000.

G. Badrul-munir-sarwar, J. A. Karypis, J. Konstan, and . Riedl, Item-based collaborative filtering recommendation algorithms, Proceedings of the Tenth International World Wide Web Conference, vol.10, pp.285-295, 2001.

A. I. Schein, A. Popescul, L. H. Ungar, and D. M. Pennock, Methods and metrics for cold-start recommendations, Proceedings of SIGIR, p.105, 2002.
DOI : 10.1145/564418.564421
URL : https://repository.upenn.edu/cgi/viewcontent.cgi?article=1141&context=cis_papers

G. Shani, D. Heckerman, and R. I. Brafman, An mdp-based recommender system, Journal of Machine Learning Research, vol.6, p.50, 2005.

J. Shawe, -. Taylor, and N. Cristianini, Kernel Methods for Pattern Analysis, 2004.

N. Shazeer, R. Doherty, C. Evans, and C. Waterson, Swivel: Improving embeddings by noticing what's missing, p.26, 2016.

L. Shi, Trading-off among accuracy, similarity, diversity, and long-tail: a graph-based recommendation approach, Proceedings of RecSys, p.28, 2013.
DOI : 10.1145/2507157.2507165

Y. Shi, M. Larson, and A. Hanjalic, List-wise learning to rank with matrix factorization for collaborative filtering, Proceedings of RecSys, p.21, 2010.

Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson, N. Oliver et al., Climf: learning to maximize reciprocal rank with collaborative lessis-more filtering, Sixth ACM Conference on Recommender Systems, RecSys, p.21, 2012.

Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson, and A. Hanjalic, xclimf: optimizing expected reciprocal rank for data with multiple levels of relevance, Seventh ACM Conference on Recommender Systems, RecSys, pp.431-434, 2013.

S. Sidana, S. Mishra, S. Amer-yahia, M. Clausel, and M. Amini, Health monitoring on social media over time, SIGIR, vol.51, p.59, 2016.
DOI : 10.1145/2911451.2914697
URL : https://hal.archives-ouvertes.fr/hal-02000482

S. Sidana, C. Laclau, M. Amini, G. Vandelle, and A. Boiscrettez, KASANDR: A large-scale dataset with implicit feedback for recommenda-REFERENCES REFERENCES tion, Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, vol.38, p.83, 2017.

S. Sidana, S. Amer-yahia, and M. Clausel, Health monitoring on social media over time
DOI : 10.1145/2911451.2914697
URL : https://hal.archives-ouvertes.fr/hal-02000482

, Knowl. Data Eng, vol.30, issue.8, p.59, 2018.

S. Sidana, C. Laclau, and M. Amini, Learning to recommend diverse items over implicit feedback on PANDOR, Proceedings of the 12th ACM Conference on Recommender Systems, RecSys, p.38, 2018.

E. Smirnova and F. Vasile, Contextual sequence modeling for recommendation with recurrent neural networks, Proceedings of the 2nd Workshop on Deep Learning for Recommender Systems, vol.24, p.50, 2017.

B. Smyth and P. Mcclave, Similarity vs. diversity, Proceedings of International Conference on Case-Based Reasoning, ICCBR, vol.6, p.28, 2001.

D. H. Stern, R. Herbrich, and T. Graepel, Matchbox: large scale online bayesian recommendations, Proceedings of WWW, p.105, 2009.

R. Su, K. Li'ang-yin, Y. Chen, and . Yu, Set-oriented personalized ranking for diversified top-n recommendation, Proceedings of RecSys, p.29, 2013.

A. Suglia, C. Greco, C. Musto, P. Marco-de-gemmis, G. Lops et al., A deep architecture for content-based recommendations exploiting recurrent neural networks, Proceedings of UMAP, pp.202-211

G. Takács and D. Tikk, Alternating least squares for personalized ranking, Proceedings of RecSys, vol.23, p.96, 2012.

G. Takács and I. Pilászy, Bottyán Németh, and Domonkos Tikk. Major components of the gravity recommendation system, SIGKDD Explorations, vol.9, p.16, 2007.

X. Yong-kiam-tan, Y. Xu, and . Liu, Improved recurrent neural networks for session-based recommendations. CoRR, abs/1606.08117, vol.24, p.50, 2016.

L. Tang, Y. Jiang, L. Li, and T. Li, Ensemble contextual bandits for personalized recommendation, RecSys, p.30, 2014.

B. Twardowski, Modelling contextual information in session-aware recommender systems with neural networks, Proceedings of the 10th ACM Conference on Recommender Systems, vol.24, p.50, 2016.

N. Usunier, M. Amini, and P. Gallinari, A data-dependent generalisation error bound for the AUC, ICML workshop on ROC Analysis in Machine Learning, p.65, 2005.
URL : https://hal.archives-ouvertes.fr/hal-01416673

N. Usunier, M. Amini, and P. Gallinari, Generalization error bounds for classifiers trained with interdependent data, Proceedings of NIPS, vol.67, p.68, 2006.

A. Van-den-oord, S. Dieleman, and B. Schrauwen, Deep content-based music recommendation, Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held, p.24, 2013.

V. Vapnik, The nature of statistical learning theory, p.71, 2000.

F. Vasile, E. Smirnova, and A. Conneau, Meta-prod2vec: Product embeddings using side-information for recommendation, Proceedings of RecSys, p.27, 2016.

F. Vasile, E. Smirnova, and A. Conneau, Meta-prod2vec: Product embeddings using side-information for recommendation, Proceedings of RecSys, p.27, 2016.

M. Volkovs and G. Yu, Effective latent models for binary feedback in recommender systems, Proceedings of SIGIR, p.71, 2015.

M. Volkovs and R. S. Zemel, Collaborative ranking with 17 parameters, Advances in Neural Information Processing Systems, pp.2303-2311, 2012.

H. Wang, N. Wang, and D. Yeung, Collaborative deep learning for recommender systems, CoRR, p.24, 2014.

H. Wang, X. Shi, and D. Yeung, Collaborative recurrent autoencoder: Recommend while learning to fill in the blanks, CoRR, p.24, 2016.

Y. Wang, E. Agichtein, and M. Benzi, TM-LDA: Efficient Online Modeling of Latent Topic Transitions in Social Media, KDD'12, p.55, 2012.

J. Wasilewski and N. Hurley, Incorporating diversity in a learning to rank recommender system, Proceedings of FLAIRS, vol.29, p.97, 2016.

J. Wasilewski and N. Hurley, Intent-aware diversification using a constrained PLSA, Proceedings of RecSys, p.29, 2016.

M. Weimer, A. Karatzoglou, Q. V. Le, and A. J. Smola, COFI RANK -maximum margin matrix factorization for collaborative ranking, Advances in Neural Information Processing Systems, p.21, 2007.

Y. Wu, C. Dubois, A. X. Zheng, and M. Ester, Collaborative denoising auto-encoders for top-n recommender systems, Proceedings of the International Conference on Web Search and Data Mining, p.24, 2016.

Z. Xing, X. Wang, and Y. Wang, Enhancing collaborative filtering music recommendation by balancing exploration and exploitation, ISMIR, p.30, 2014.

J. Xu and H. Li, Adarank: a boosting algorithm for information retrieval, Proceedings of SIGIR, p.21, 2007.

J. Xu, T. Liu, M. Lu, H. Li, and W. Ma, Directly optimizing evaluation measures in learning to rank, Proceedings of SIGIR, p.21, 2008.

M. Zhang and N. Hurley, Avoiding monotony: improving the diversity of recommendation lists, Proceedings of RecSys, vol.6, p.28, 2008.

M. Zhang and N. Hurley, Novel item recommendation by user profile partitioning, 2009 IEEE/WIC/ACM International Conference on Web Intelligence, p.28, 2009.

X. Zhao, W. Zhang, and J. Wang, Interactive collaborative filtering, Proceedings of the 22nd ACM international conference on Conference on information & knowledge management, CIKM '13, p.30, 2013.

G. Zheng, F. Zhang, Z. Zheng, Y. Xiang, N. J. Yuan et al., DRN: A deep reinforcement learning framework for news recommendation, Proceedings of the 2018 World Wide Web Conference on World Wide Web, p.30, 2018.

Y. Zheng, B. Tang, W. Ding, and H. Zhou, A neural autoregressive approach to collaborative filtering, p.24, 2016.

Y. Zhou, D. M. Wilkinson, R. Schreiber, and R. Pan, Large-scale parallel collaborative filtering for the netflix prize, Algorithmic Aspects in Information and Management, 4th International Conference, AAIM, p.16, 2008.

C. Ziegler, S. M. Mcnee, J. A. Konstan, and G. Lausen, Improving recommendation lists through topic diversification, Proceedings of WWW, p.28, 2005.

A. Zimdars, D. M. Chickering, and C. Meek, Using temporal data for making recommendations, p.50, 2013.