S. Functions and .. , 53 3.2.2 Submodular Function Maximization, p.55

.. Submodular-diversity-function, 56 3.3.1 Utility-Weighted Coverage for Relevant Diverse Sets, p.57

.. Coverage-of-a-node, 57 3.3.3 Utility-Weighted Coverage of a Set of Nodes, p.57

N. Abe, B. Zadrozny, and J. Langford, An iterative method for multi-class cost-sensitive learning, Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '04, pp.3-11, 2004.
DOI : 10.1145/1014052.1014056

URL : http://hunch.net/~jl/projects/reductions/mc2/p542-Abe.ps

R. Agrawal, S. Gollapudi, A. Halverson, and S. Ieong, Diversifying search results, Proceedings of the Second ACM International Conference on Web Search and Data Mining, WSDM '09, p.64, 2009.
DOI : 10.1145/1498759.1498766

URL : http://research.microsoft.com/pubs/73931/diversifying-wsdm09.pdf

M. Anthony and P. L. Bartlett, Neural Network Learning: Theoretical Foundations, 2009.
DOI : 10.1017/CBO9780511624216

A. Ashkan, B. Kveton, S. Berkovsky, and Z. Wen, Optimal greedy diversity for recommendation, IJCAI, pp.2015-63

F. Bach, Learning with Submodular Functions: A Convex Optimization Perspective . Foundations and trends in machine learning, p.52, 2013.
DOI : 10.1561/2200000039

URL : https://hal.archives-ouvertes.fr/hal-00645271

F. R. Bach, D. Heckerman, and E. Horvitz, Considering cost asymmetry in learning classifiers, J. Mach. Learn. Res, vol.7, issue.46, pp.1713-1741, 2006.

A. Badanidiyuru, B. Mirzasoleiman, A. Karbasi, and A. Krause, Streaming submodular maximization, Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '14, pp.671-680
DOI : 10.1145/2623330.2623637

L. Boratto and S. Carta, State-of-the-Art in Group Recommendation and New Approaches for Automatic Identification of Groups, Information retrieval and mining in distributed environments, pp.1-20
DOI : 10.1007/978-3-642-16089-9_1

A. Borodin, H. C. Lee, and Y. Ye, Max-sum diversification, monotone submodular functions and dynamic updates, PODS, pp.155-166, 2012.
DOI : 10.1145/2213556.2213580

URL : http://www.cs.toronto.edu/~bor/pods-arXiv-version.pdf

S. Boyd and L. Vandenberghe, Convex Optimization, pp.10-39, 2004.

R. Busa-fekete, B. Szörényi, K. Dembczynski, and E. Hüllermeier, Online f-measure optimization, Advances in Neural Information Processing Systems, pp.595-603

A. Cambini and L. Martein, Generalized Convexity and Optimization, Lecture Notes in Economics and Mathematical Systems, vol.616, issue.26, p.25, 2009.

J. Carbonell and J. Goldstein, The use of MMR, diversity-based reranking for reordering documents and producing summaries, Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval , SIGIR '98, pp.335-336, 1998.
DOI : 10.1145/290941.291025

N. Cesa-bianchi and G. Lugosi, Prediction, learning, and games, 2006.
DOI : 10.1017/CBO9780511546921

C. Chang and C. Lin, LIBSVM, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.3, pp.27-28
DOI : 10.1145/1961189.1961199

O. Chapelle, B. Schölkopf, and A. Zien, Semi-supervised Learning Adaptive computation and machine learning, 2006.

O. Chapelle, S. Ji, C. Liao, E. Velipasaoglu, L. Lai et al., Intent-based diversification of web search results: metrics and algorithms, Information Retrieval, vol.20, issue.4, pp.572-592, 2011.
DOI : 10.1145/582415.582418

W. Cheng, K. Dembczynski, E. Hüllermeier, A. Jaroszewicz, and W. Waegeman, F-Measure Maximization in Topical Classification, RSCTC, pp.439-446
DOI : 10.1007/978-3-642-32115-3_52

L. Charles, M. Clarke, . Kolla, V. Gordon, O. Cormack et al., Novelty and diversity in information retrieval evaluation, Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, pp.659-666, 2008.

S. Clémençon and N. Vayatis, Adaptive estimation of the optimal roc curve and a bipartite ranking algorithm, Algorithmic Learning Theory, pp.216-231, 2009.

T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction To Algorithms, 2001.

P. Cremonesi, Y. Koren, and R. Turrin, Performance of recommender algorithms on top-n recommendation tasks, Proceedings of the fourth ACM conference on Recommender systems, RecSys '10, pp.39-46
DOI : 10.1145/1864708.1864721

A. Das and D. Kempe, Submodular meets spectral: Greedy algorithms for subset selection, sparse approximation and dictionary selection, Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp.1057-1064, 2011.

K. Dembczynski, W. Waegeman, W. Cheng, and E. Hüllermeier, An exact algorithm for F-measure maximization, NIPS, pp.1404-1412, 2011.

K. Dembczynski, A. Jachnik, W. Kotlowski, W. Waegeman, and E. Hüllermeier, Optimizing the F-measure in multilabel classification: Plug-in rule approach versus structured loss minimization, ICML Conference Proceedings, pp.1130-1138, 2013.

J. Dem?ar, Statistical comparisons of classifiers over multiple data sets, Journal of Machine learning research, vol.7, issue.78, pp.1-30, 2006.

L. Devroye, L. Györfi, and G. Lugosi, A Probabilistic Theory of Pattern Recognition Applications of mathematics : stochastic modelling and applied probability, 1996.

A. Dubey, S. Chakrabarti, and C. Bhattacharyya, Diversity in ranking via resistive graph centers, Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '11, pp.78-86
DOI : 10.1145/2020408.2020428

URL : http://www.cse.iitb.ac.in/~soumen/doc/sigkdd2011/GCD.pdf

J. Edmonds, Matroids and the greedy algorithm Mathematical programming, pp.127-136, 1971.

M. Ehrgott and X. Gandibleux, Multiple Criteria Optimization. State of the art annotated bibliographic surveys, Kluwer Academic, vol.52, p.12, 2002.
DOI : 10.1007/b101915

URL : https://hal.archives-ouvertes.fr/hal-00470381

C. Elkan, The foundations of cost-sensitive learning, International Joint Conference on Artificial Intelligence, pp.973-978, 2001.

E. Rong, C. J. Fan, and . Lin, A study on threshold selection for multi-label classification, 2007.

K. Rong-en-fan, C. Chang, X. Hsieh, C. Wang, and . Lin, Liblinear: A library for large linear classification, The Journal of Machine Learning Research, vol.9, pp.1871-1874, 2008.

M. Frank and P. Wolfe, An algorithm for quadratic programming Naval research logistics quarterly, pp.95-110, 1956.

A. Fujino, H. Isozaki, and J. Suzuki, Multi-label text categorization with model combination based on f1score maximization, Proceedings of IJCNLP, pp.823-828, 2008.

S. Fujishige, Submodular functions and optimization, pp.52-53, 2005.

S. Gollapudi and A. Sharma, An axiomatic approach for result diversification, Proceedings of the 18th international conference on World wide web, WWW '09, pp.381-390, 2009.
DOI : 10.1145/1526709.1526761

URL : http://www2009.org/proceedings/pdf/p381.pdf

Y. Grandvalet, J. Mariéthoz, and S. Bengio, A probabilistic interpretation of SVMs with an application to unbalanced classification, NIPS, 2005

J. He, H. Tong, Q. Mei, and B. Szymanski, Gender: A generic diversified ranking algorithm, NIPS, pp.1142-1150, 2012.

M. Hollander and D. A. Wolfe, Nonparametric statistical methods Wiley Series in Probability and Statistics -Applied Probability and Statistics Section, p.77, 1973.

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

URL : http://www2.research.att.com/%7Eyifanhu/PUB/cf.pdf

J. Neil and . Hurley, Personalised ranking with diversity, RecSys, pp.379-382, 2013.

M. Jansche, Maximum expected F-measure training of logistic regression models, Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing , HLT '05, p.20, 2005.
DOI : 10.3115/1220575.1220662

M. Jansche, A maximum expected utility framework for binary sequence labeling, ACL. The Association for Computational Linguistics, p.20, 2007.

T. Joachims, A support vector method for multivariate performance measures, Proceedings of the 22nd international conference on Machine learning , ICML '05, pp.377-384, 2005.
DOI : 10.1145/1102351.1102399

L. Kaufman, J. Peter, and . Rousseeuw, Finding groups in data: an introduction to cluster analysis, 2009.
DOI : 10.1002/9780470316801

J. Kim, Y. Wang, and Y. Yasunori, The genia event extraction shared task, 2013 edition -overview, Proceedings of the BioNLP Shared Task 2013 Workshop, pp.8-15, 2013.
DOI : 10.1186/1471-2105-13-s11-s1

URL : http://doi.org/10.1186/1471-2105-13-s11-s1

S. Kim, A. Magnani, S. Samar, S. Boyd, and J. Lim, Pareto optimal linear classification, Proceedings of the 23rd international conference on Machine learning , ICML '06, pp.473-480, 2006.
DOI : 10.1145/1143844.1143904

URL : http://stanford.edu/~boyd/papers/pdf/pareto_opt_class_icml.pdf

O. Oluwasanmi, N. Koyejo, . Natarajan, K. Pradeep, . Ravikumar et al., Consistent binary classification with generalized performance metrics, Advances in Neural Information Processing Systems 27, pp.2744-2752, 2014.

O. Oluwasanmi, N. Koyejo, . Natarajan, K. Pradeep, . Ravikumar et al., Consistent multilabel classification, Advances in Neural Information Processing Systems, pp.3321-3329, 2015.

A. Krause and D. Golovin, Submodular function maximization . Tractability: Practical Approaches to Hard Problems, pp.8-2012
DOI : 10.1017/cbo9781139177801.004

URL : http://www.cs.cmu.edu/%7Edgolovin/papers/submodular_survey12.pdf

A. Krause, G. Ryan, and . Gomes, Budgeted nonparametric learning from data streams, Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp.391-398, 2010.

J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J. Vanbriesen et al., Cost-effective outbreak detection in networks, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '07, pp.420-429, 2007.
DOI : 10.1145/1281192.1281239

URL : http://repository.cmu.edu/cgi/viewcontent.cgi?article=1527&context=compsci

H. Lin and J. Bilmes, A class of submodular functions for document summarization, ACL, pp.510-520

Z. C. Lipton, C. Elkan, and B. Naryanaswamy, Optimal Thresholding of Classifiers to Maximize F1 Measure, Machine Learning and Knowledge Discovery in Databases, pp.225-239
DOI : 10.1007/978-3-662-44851-9_15

Q. Mei, J. Guo, and D. Radev, DivRank, Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '10, pp.1009-1018
DOI : 10.1145/1835804.1835931

M. Minoux, Accelerated greedy algorithms for maximizing submodular set functions, Optimization Techniques, pp.234-243, 1978.
DOI : 10.1007/BFb0006528

R. David, V. Musicant, A. Kumar, and . Ozgur, Optimizing Fmeasure with support vector machines, Proceedings of the FLAIRS Conference, pp.356-360, 2003.

Y. Nan, K. M. , A. Chai, W. Sun-lee, and H. L. Chieu, Optimizing F-measure: A tale of two approaches, ICML. icml.cc / Omnipress, pp.20-22, 2012.

H. Narasimhan, R. Vaish, and S. Agarwal, On the statistical consistency of plug-in classifiers for non-decomposable performance measures, Advances in Neural Information Processing Systems 27, pp.1493-1501, 2014.

H. Narasimhan, H. Ramaswamy, A. Saha, and S. Agarwal, Consistent multiclass algorithms for complex performance measures, Proceedings of the 32nd International Conference on Machine Learning (ICML-15), pp.2398-2407, 2015.

L. George, . Nemhauser, A. Laurence, . Wolsey, L. Marshall et al., An analysis of approximations for maximizing submodular set functions, Mathematical Programming, vol.14, issue.1, pp.1978-56

J. Oh, S. Park, H. Yu, M. Song, and S. Park, Novel Recommendation Based on Personal Popularity Tendency, 2011 IEEE 11th International Conference on Data Mining, pp.507-516, 2011.
DOI : 10.1109/ICDM.2011.110

N. Shameem-puthiya-parambath, Y. Usunier, and . Grandvalet, Optimizing F-measures by cost-sensitive classification, Advances in Neural Information Processing Systems 27, pp.2123-2131, 2014.

J. Petterson, S. Tibério, and . Caetano, Reverse multi-label learning, NIPS, 1912.

J. Petterson, S. Tibério, and . Caetano, Submodular multi-label learning, NIPS, pp.1512-1520

I. Pillai, G. Fumera, and F. Roli, F-measure optimisation in multi-label classifiers, ICPR, pp.2424-2427
DOI : 10.1016/j.patcog.2013.01.012

B. Pradel, N. Usunier, and P. Gallinari, Ranking with nonrandom missing ratings: influence of popularity and positivity on evaluation metrics, RecSys, pp.147-154
DOI : 10.1145/2365952.2365982

H. William, S. A. Press, W. T. Teukolsky, B. P. Vetterling, and . Flannery, Numerical Recipes 3rd Edition: The Art of Scientific Computing, pp.9780521880688-46, 2007.

C. Qian, Y. Yu, and Z. Zhou, Subset selection by pareto optimization, Advances in Neural Information Processing Systems, pp.1774-1782

F. Radlinski and S. Dumais, Improving personalized web search using result diversification, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval , SIGIR '06, pp.691-692, 2006.
DOI : 10.1145/1148170.1148320

URL : http://www.cs.cornell.edu/%7Efilip/paper.php?p=SIGIR06Diversification.pdf

F. Radlinski, R. Kleinberg, and T. Joachims, Learning diverse rankings with multi-armed bandits, Proceedings of the 25th international conference on Machine learning, ICML '08, pp.784-791
DOI : 10.1145/1390156.1390255

URL : http://www.cs.cornell.edu/People/tj/publications/radlinski_etal_08a.pdf

F. Radlinski, N. Paul, B. Bennett, T. Carterette, and . Joachims, Redundancy, diversity and interdependent document relevance, SI- GIR Forum, pp.46-52, 2009.
DOI : 10.1145/1670564.1670572

URL : http://www.sigir.org/forum/2009D/sigirwksp/2009d_sigirforum_radlinski.pdf

K. Raman, P. Shivaswamy, and T. Joachims, Online learning to diversify from implicit feedback, Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '12, pp.705-713
DOI : 10.1145/2339530.2339642

URL : http://www.cs.cornell.edu/People/tj/publications/raman_etal_12b.pdf

D. Mark, . Reid, C. Robert, and . Williamson, Composite binary losses, Journal of Machine Learning Research, vol.11, pp.2387-2422, 2010.

L. Rosasco, E. De, V. A. Caponnetto, M. Piana, and A. Verri, Are Loss Functions All the Same?, Neural Computation, vol.16, issue.5, 2004.
DOI : 10.1006/jcom.2002.0635

URL : http://www.dima.unige.it/~piana/files/loss.pdf

W. Rudin, Functional analysis. International Series in Pure and Applied Mathematics, 1991.

L. Rodrygo, C. Santos, I. Macdonald, and . Ounis, Exploiting query reformulations for web search result diversification, p.64, 2010.

B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, Itembased collaborative filtering recommendation algorithms, WWW

C. Scott, Calibrated asymmetric surrogate losses, Electronic Journal of Statistics, vol.6, issue.0, pp.958-992, 2012.
DOI : 10.1214/12-EJS699

URL : http://doi.org/10.1214/12-ejs699

K. Spärck-jones, E. Stephen, M. Robertson, and . Sanderson, Ambiguous requests, SIGIR Forum, 2007.
DOI : 10.1145/1328964.1328965

H. Steck, Training and testing of recommender systems on data missing not at random, Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '10, pp.713-722
DOI : 10.1145/1835804.1835895

H. Steck, Item popularity and recommendation accuracy, Proceedings of the fifth ACM conference on Recommender systems, RecSys '11, pp.125-132
DOI : 10.1145/2043932.2043957

H. Steck, Evaluation of recommendations, Proceedings of the 7th ACM conference on Recommender systems, RecSys '13, pp.213-220
DOI : 10.1145/2507157.2507160

I. Steinwart, How to Compare Different Loss Functions and Their Risks, Constructive Approximation, vol.26, issue.2, pp.225-287, 2007.
DOI : 10.1007/s00365-006-0662-3

URL : http://www.c3.lanl.gov/ml/pubs/2005_loss/paper.pdf

R. Su, K. Li-'ang-yin, Y. Chen, and . Yu, Set-oriented personalized ranking for diversified top-n recommendation, Proceedings of the 7th ACM conference on Recommender systems, RecSys '13, pp.415-418, 2013.
DOI : 10.1145/2507157.2507207

G. Takács and D. Tikk, Alternating least squares for personalized ranking, Proceedings of the sixth ACM conference on Recommender systems, RecSys '12, pp.83-90
DOI : 10.1145/2365952.2365972

H. Tong, J. He, Z. Wen, R. Konuru, and C. Lin, Diversified ranking on large graphs, Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '11, pp.1028-1036
DOI : 10.1145/2020408.2020573

I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun, Large margin methods for structured and interdependent output variables, Journal of Machine Learning Research, vol.6, pp.1453-1484, 2005.

G. Tsoumakas and I. Katakis, Multi-label classification: An overview, International Journal of Data Warehousing and Mining (IJDWM), vol.3, issue.3, pp.1-13, 2007.
DOI : 10.4018/978-1-59904-951-9.ch006

S. Vargas and P. Castells, Rank and relevance in novelty and diversity metrics for recommender systems, Proceedings of the fifth ACM conference on Recommender systems, RecSys '11, pp.109-116
DOI : 10.1145/2043932.2043955

URL : http://ir.ii.uam.es/predict/pubs/recsys11-vargas.pdf

S. Vargas, L. Baltrunas, A. Karatzoglou, and P. Castells, Coverage, redundancy and size-awareness in genre diversity for recommender systems, Proceedings of the 8th ACM Conference on Recommender systems, RecSys '14, pp.209-216
DOI : 10.1145/2645710.2645743

W. Waegeman, K. Dembczy´nskidembczy´-dembczy´nski, A. Jachnik, W. Cheng, and E. Hüllermeier, On the bayes-optimality of F-measure maximizers, Journal of Machine Learning Research, vol.15, issue.38, pp.3333-3388, 2014.

L. Wu, Q. Liu, E. Chen, N. J. Yuan, G. Guo et al., Relevance Meets Coverage, ACM Transactions on Intelligent Systems and Technology, vol.7, issue.3, pp.2016-63
DOI : 10.1145/1060745.1060754

Y. Yue and T. Joachims, Predicting diverse subsets using structural SVMs, Proceedings of the 25th international conference on Machine learning, ICML '08, pp.1224-1231, 2008.
DOI : 10.1145/1390156.1390310

URL : http://www.cs.cornell.edu/people/tj/publications/yue_joachims_08a.pdf

. Cheng-xiang-zhai, W. William, J. Cohen, and . Lafferty, Beyond independent relevance: methods and evaluation metrics for subtopic retrieval, SIGIR, pp.10-17, 2003.

B. Zhang, H. Li, Y. Liu, L. Ji, W. Xi et al., Improving web search results using affinity graph, Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '05, pp.504-511, 2005.
DOI : 10.1145/1076034.1076120

URL : http://web.cse.msu.edu/~liuyi3/publications/zhang-05-improving.pdf

M. Zhang and N. Hurley, Avoiding monotony, Proceedings of the 2008 ACM conference on Recommender systems, RecSys '08, pp.123-130, 2008.
DOI : 10.1145/1454008.1454030

Z. Zhou and X. Liu, ON MULTI-CLASS COST-SENSITIVE LEARNING, Computational Intelligence, vol.18, issue.1, pp.232-257, 2010.
DOI : 10.1111/j.1467-8640.2010.00358.x

URL : http://www.aaai.org/Papers/AAAI/2006/AAAI06-091.pdf

X. Zhu, B. Andrew, J. Goldberg, D. Van-gael, and . Andrzejewski, Improving diversity in ranking using absorbing random walks, HLT-NAACL, pp.97-104, 2007.

C. Ziegler, M. Sean, . Mcnee, A. Joseph, G. Konstan et al., Improving recommendation lists through topic diversification, Proceedings of the 14th international conference on World Wide Web , WWW '05, pp.22-32, 2005.
DOI : 10.1145/1060745.1060754

URL : http://www.informatik.uni-freiburg.de/~cziegler/papers/WWW-05-CR.pdf