R. Agrawal, A. Gupta, Y. Prabhu, and M. Varma, Multilabel learning with millions of labels: Recommending advertiser bid phrases for web pages, Proceedings of the 22nd international conference on World Wide Web, pp.13-24, 2013.

A. Appleby, Murmurhash 2, 2008.

D. Arthur and S. Vassilvitskii, The advantages of careful seeding, Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, pp.1027-1035, 2007.

R. Babbar and B. Schölkopf, Dismec: Distributed sparse machines for extreme multi-label classification, Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp.721-729, 2017.

S. Bengio, J. Weston, and D. Grangier, Label embedding trees for large multi-class tasks, Advances in Neural Information Processing Systems, pp.163-171, 2010.

K. Bhatia, H. Jain, P. Kar, M. Varma, J. et al., Sparse local embeddings for extreme multi-label classification, Advances in neural information processing systems, pp.730-738, 2015.

L. Breiman, Random forests, Machine learning, vol.45, issue.1, pp.5-32, 2001.

J. Dean, D. Patterson, Y. , and C. , A new golden age in computer architecture: Empowering the machine learning revolution, IEEE Micro, 2018.

J. Deng, W. Dong, R. Socher, L. Li, K. Li et al., Imagenet: A large-scale hierarchical image database, IEEE Conference on Computer Vision and Pattern Recognition, pp.248-255, 2009.

J. Deng, S. Satheesh, A. C. Berg, L. , and F. , Fast and balanced: Efficient label tree learning for large scale object recognition, Advances in Neural Information Processing Systems, pp.567-575, 2011.

I. A. Hashem, I. Yaqoob, N. B. Anuar, S. Mokhtar, A. Gani et al., The rise of big data on cloud computing: Review and open research issues. Information Systems, vol.47, pp.98-115, 2015.

H. Jain, Y. Prabhu, and M. Varma, Extreme multi-label loss functions for recommendation, tagging, ranking & other missing label applications, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.935-944, 2016.

K. Jasinska, K. Dembczynski, R. Busa-fekete, K. Pfannschmidt, T. Klerx et al., Extreme f-measure maximization using sparse probability estimates, International Conference on Machine Learning, pp.1435-1444, 2016.

K. Kambatla, G. Kollias, V. Kumar, and A. Grama, Trends in big data analytics, Journal of Parallel and Distributed Computing, vol.74, issue.7, pp.2561-2573, 2014.

D. Kocev, C. Vens, J. Struyf, and S. D?eroski, Ensembles of multi-objective decision trees, Machine Learning: ECML 2007, pp.624-631, 2007.

J. Mcauley, R. Pandey, and J. Leskovec, Inferring networks of substitutable and complementary products, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.785-794, 2015.

I. Partalas, A. Kosmopoulos, N. Baskiotis, T. Artieres, G. Paliouras et al., A benchmark for largescale text classification, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01691460

Y. Prabhu and M. Varma, Fastxml: A fast, accurate and stable tree-classifier for extreme multi-label learning, Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.263-272, 2014.

Y. Tagami, Annexml: Approximate nearest neighbor search for extreme multi-label classification, Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.455-464, 2017.

G. Tsoumakas and I. Katakis, Multi-label classification: An overview, International Journal of Data Warehousing and Mining, vol.3, issue.3, 2006.

G. Tsoumakas, I. Katakis, and I. Vlahavas, Effective and efficient multilabel classification in domains with large number of labels, Proc. ECML/PKDD 2008 Workshop on Mining Multidimensional Data (MMD08), pp.30-44, 2008.

K. Weinberger, A. Dasgupta, J. Langford, A. Smola, A. et al., Feature hashing for large scale multitask learning, International Conference on Machine Learning, pp.1113-1120, 2009.
DOI : 10.1145/1553374.1553516

URL : http://arxiv.org/pdf/0902.2206

J. Weston, S. Bengio, and N. Usunier, Wsabie: Scaling up to large vocabulary image annotation, International Joint Conference on Artificial Intelligence, vol.11, pp.2764-2770, 2011.

J. Weston, A. Makadia, Y. , and H. , Label partitioning for sublinear ranking, International Conference on Machine Learning, pp.181-189, 2013.

I. E. Yen, X. Huang, W. Dai, P. Ravikumar, I. Dhillon et al., Ppdsparse: A parallel primal-dual sparse method for extreme classification, Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.545-553, 2017.

I. E. Yen, X. Huang, P. Ravikumar, K. Zhong, and I. Dhillon, Pd-sparse: A primal and dual sparse approach to extreme multiclass and multilabel classification, International Conference on Machine Learning, pp.3069-3077, 2016.

H. Yu, P. Jain, P. Kar, and I. Dhillon, Large-scale multi-label learning with missing labels, International Conference on Machine Learning, pp.593-601, 2014.

M. Zhang and Z. Zhou, Ml-knn: A lazy learning approach to multi-label learning, Pattern recognition, vol.40, issue.7, pp.2038-2048, 2007.

M. Zhang and Z. Zhou, A review on multi-label learning algorithms, IEEE transactions on knowledge and data engineering, vol.26, issue.8, pp.1819-1837, 2014.