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Learning to rank is a machine learning technique broadly used in many areas such as document retrieval, collaborative filtering or question answering.
The newton-raphson method
E. Whittaker and G. Robinson · 1967
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Learning with many irrelevant features
H. Almuallim and T. G. Dietterich · 1991
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Efficiently inducing determinations: A complete and systematic search algorithm that uses optimal pruning
J. C. Schlimmer · 1993
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Learning boolean concepts in the presence of many irrelevant features
H. Almuallim and T. G. Dietterich · 1994
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Oblivious decision trees and abstract cases
P. Langley and S. Sage · 1994
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Oblivious decision trees, graphs, and top-down pruning
R. Kohavi and C.-H. Li · 1995
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Targeting business users with decision table classifiers
R. Kohavi and D. Sommerfield · 1998
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Greedy function approximation: A gradient boosting machine
J. H. Friedman · 2000
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Large margin rank boundaries for ordinal regression
R. Herbrich, T. Graepel, and K. Obermayer · 2000
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Random forests
L. Breiman · 2001
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Pranking with ranking
K. Crammer and Y. Singer · 2001
Cited alongside, same era.
Cumulated gain-based evaluation of ir techniques
K. Järvelin and J. Kekäläinen · 2002
Cited alongside, same era.
Improving stability of decision trees
M. Last, O. Maimon, and E. Minkov · 2002
Cited alongside, same era.
Improving supervised learning by feature decomposition
O. Maimon and L. Rokach · 2002
Cited alongside, same era.
An efficient boosting algorithm for combining preferences
Y. Freund, R. Iyer, R. E. Schapire, and Y. Singer · 2003
Cited alongside, same era.
Learning to rank using gradient descent
C. Burges, T. Shaked, E. Renshaw, M. Deeds, N. Hamilton, and G. Hullender · 2005
Cited alongside, same era.
Learning to Rank with Nonsmooth Cost Functions
Query-level loss functions for information retrieval
T. Qin, X.-D. Zhang, M.-F. Tsai, D.-S. Wang, T.-Y. Liu, and H. Li · 2008
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Oblivious decision trees
L. Rokach and O. Maimon · 2008
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Listwise approach to learning to rank: Theory and algorithm
F. Xia, T.-Y. Liu, J. Wang, W. Zhang, and H. Li · 2008
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Directly optimizing evaluation measures in learning to rank
J. Xu, T. yan Liu, M. Lu, H. Li, and W. ying Ma · 2008
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From RankNet to LambdaRank to LambdaMART: An overview
C. J. C. Burges · 2010
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Adapting boosting for information retrieval measures
Q. Wu, C. J. Burges, K. M. Svore, and J. Gao · 2010
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C. J. C. Burges, R. Ragno, and Q. V. Le · 2006
Cited alongside, same era.
Learning to rank: From pairwise approach to listwise approach
Z. Cao, T. Qin, T.-Y. Liu, M.-F. Tsai, and H. Li · 2007
Cited alongside, same era.
Mcrank: Learning to rank using multiple classification and gradient boosting
P. Li, C. J. C. Burges, and Q. Wu · 2007
Cited alongside, same era.
Adarank: A boosting algorithm for information retrieval
J. Xu and H. Li · 2007
Cited alongside, same era.
Winning the transfer learning track of yahoo!’s learning to rank challenge with yetirank
A. Gulin, I. Kuralenok, and D. Pavlov · 2011
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A short introduction to learning to rank
L. Hang · 2011
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Facebook Code Blog, 2016
Introducing FBLearner Flow: Facebook’s AI backbone · 2016
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