A. Baccini, S. Déjean, L. Lafage, and J. Mothe, How many performance measures to evaluate information retrieval systems?, Knowledge and Information Systems, vol.13, issue.3, p.2012
DOI : 10.1002/9780470316641

URL : https://hal.archives-ouvertes.fr/hal-00658732

A. Bigot, S. Déjean, and J. Mothe, Learning to Choose the Best System Configuration in Information Retrieval: the Case of Repeated Queries, Journal of Universal Computer Science, vol.21, issue.13, pp.1726-1745, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01592024

L. Breiman, Random forests, Machine Learning, pp.5-32, 2001.

D. Carmel and E. Yom-tov, Estimating the query difficulty for information retrieval, Synthesis Lectures on Information Concepts, Retrieval, and Services, pp.1-89, 2010.
DOI : 10.2200/s00235ed1v01y201004icr015

J. H. Friedman, machine., The Annals of Statistics, vol.29, issue.5, pp.1189-1232, 2000.
DOI : 10.1214/aos/1013203451

C. Hauff, D. Hiemstra, F. De, and J. , A survey of pre-retrieval query performance predictors, Proceeding of the 17th ACM conference on Information and knowledge mining, CIKM '08, 2008.
DOI : 10.1145/1458082.1458311

T. Joachims, Optimizing search engines using clickthrough data, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '02, 2002.
DOI : 10.1145/775047.775067

T. Liu, Learning to Rank for Information Retrieval, Foundations and Trends?? in Information Retrieval, vol.3, issue.3, pp.225-331, 2009.
DOI : 10.1561/1500000016

C. Macdonald, R. L. Santos, I. Ounis, and B. He, About learning models with multiple query-dependent features, ACM Transactions on Information Systems, vol.31, issue.3, pp.31-2013
DOI : 10.1145/2493175.2493176

J. Mothe and L. Tanguy, Linguistic features to predict query difficulty, Proc. of SIGIR, 2005.
URL : https://hal.archives-ouvertes.fr/halshs-00287692

I. Ounis, G. Amati, V. Plachouras, B. He, C. Macdonald et al., Terrier: A High Performance and Scalable Information Retrieval Platform, Proc. of OSIR, 2006.
DOI : 10.1007/978-3-540-31865-1_37

URL : http://eprints.gla.ac.uk/3773/1/ounis3773.pdf

A. Shtok, O. Kurland, D. Carmel, F. Raiber, and G. Markovits, Predicting query performance by query-drift estimation, ACM Transactions on Information Systems, vol.3011, issue.2, pp.1-1135, 2012.
DOI : 10.1007/978-3-642-04417-5_30

URL : http://iew3.technion.ac.il/~kurland/qdQueryPerf.pdf

Q. Wu, C. J. Burges, K. M. Svore, and J. Gao, Adapting boosting for information retrieval measures, Information Retrieval, vol.10, issue.3, pp.254-270, 2010.
DOI : 10.1007/s10791-009-9112-1

Y. Zhou and W. B. Croft, Query performance prediction in web search environments, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '07, 2007.
DOI : 10.1145/1277741.1277835

URL : http://maroo.cs.umass.edu/pub/web/getpdf.php?id=726