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Online learning to rank (OL2R) has attracted great research interests in recent years, thanks to its advantages in avoiding expensive relevance labeling as required in offline supervised ranking model learning.
The probability ranking principle in ir
Stephen E Robertson · 1977
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Accurately interpreting clickthrough data as implicit feedback
Thorsten Joachims, Laura Granka, Bing Pan, Helene Hembrooke, and Geri Gay · 2005
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Improving web search ranking by incorporating user behavior information
Eugene Agichtein, Eric Brill, and Susan Dumais · 2006
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An eye tracking study of the effect of target rank on web search
Zhiwei Guan and Edward Cutrell · 2007
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An experimental comparison of click position-bias models
Nick Craswell, Onno Zoeter, Michael Taylor, and Bill Ramsey · 2008
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Learning diverse rankings with multi-armed bandits
Filip Radlinski, Robert Kleinberg, and Thorsten Joachims · 2008
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Efficient multiple-click models in web search
Fan Guo, Chao Liu, and Yi Min Wang · 2009
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Interactively optimizing information retrieval systems as a dueling bandits problem
Yisong Yue and Thorsten Joachims · 2009
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Yahoo! learning to rank challenge overview
Olivier Chapelle and Yi Chang · 2011
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Estimating interleaved comparison outcomes from historical click data
Katja Hofmann, Shimon Whiteson, and Maarten de Rijke · 2012
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Introducing letor 4.0 datasets, 2013
Tao Qin and Tie-Yan Liu · 2013
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Content-aware click modeling
Hongning Wang, ChengXiang Zhai, Anlei Dong, and Yi Chang · 2013
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Multileaved comparisons for fast online evaluation
Anne Schuth, Floor Sietsma, Shimon Whiteson, Damien Lefortier, and Maarten de Rijke · 2014
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Exploiting contextual factors for click modeling in sponsored search
Dawei Yin, Shike Mei, Bin Cao, Jian-Tao Sun, and Brian D Davison · 2014
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Cascading bandits: Learning to rank in the cascade model
Branislav Kveton, Csaba Szepesvari, Zheng Wen, and Azin Ashkan · 2015
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Dcm bandits: Learning to rank with multiple clicks
Sumeet Katariya, Branislav Kveton, Csaba Szepesvari, and Zheng Wen · 2016
Cited alongside, same era.
Contextual combinatorial cascading bandits
Shuai Li, Baoxiang Wang, Shengyu Zhang, and Wei Chen · 2016
Cited alongside, same era.
Constructing reliable gradient exploration for online learning to rank
Tong Zhao and Irwin King · 2016
Cited alongside, same era.
Ranking with fairness constraints
L Elisa Celis, Damian Straszak, and Nisheeth K Vishnoi · 2017
Cited alongside, same era.
Balancing speed and quality in online learning to rank for information retrieval
Harrie Oosterhuis and Maarten de Rijke · 2017
Cited alongside, same era.
Measuring fairness in ranked outputs
Ke Yang and Julia Stoyanovich · 2017
Estimating position bias without intrusive interventions
Aman Agarwal, Ivan Zaitsev, Xuanhui Wang, Cheng Li, Marc Najork, and Thorsten Joachims · 2019
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Fairness and transparency in ranking
Carlos Castillo · 2019
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Intervention harvesting for context-dependent examination-bias estimation
Zhichong Fang, Aman Agarwal, and Thorsten Joachims · 2019
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Fairness-aware ranking in search & recommendation systems with application to linkedin talent search
Sahin Cem Geyik, Stuart Ambler, and Krishnaram Kenthapadi · 2019
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Policy learning for fairness in ranking
Ashudeep Singh and Thorsten Joachims · 2019
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Variance reduction in gradient exploration for online learning to rank
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Cited alongside, same era.
Fa* ir: A fair top-k ranking algorithm
Meike Zehlike, Francesco Bonchi, Carlos Castillo, Sara Hajian, Mohamed Megahed, and Ricardo Baeza-Yates · 2017
Cited alongside, same era.
Online learning to rank in stochastic click models
Masrour Zoghi, Tomas Tunys, Mohammad Ghavamzadeh, Branislav Kveton, Csaba Szepesvari, and Zheng Wen · 2017
Cited alongside, same era.
Equity of attention: Amortizing individual fairness in rankings
Asia J Biega, Krishna P Gummadi, and Gerhard Weikum · 2018
Cited alongside, same era.
Bubblerank: Safe online learning to rerank
Branislav Kveton, Chang Li, Tor Lattimore, Ilya Markov, Maarten de Rijke, Csaba Szepesvari, and Masrour Zoghi · 2018
Cited alongside, same era.
Toprank: A practical algorithm for online stochastic ranking
Tor Lattimore, Branislav Kveton, Shuai Li, and Csaba Szepesvari · 2018
Cited alongside, same era.
Online learning to rank with features
Shuai Li, Tor Lattimore, and Csaba Szepesvári · 2018
Cited alongside, same era.
Huazheng Wang, Sonwoo Kim, Eric McCord-Snook, Qingyun Wu, and Hongning Wang · 2019
Later among the works it cites.
Bandit algorithms
Tor Lattimore and Csaba Szepesvári · 2020
Later among the works it cites.
Controlling fairness and bias in dynamic learning-to-rank
Marco Morik, Ashudeep Singh, Jessica Hong, and Thorsten Joachims · 2020
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Reducing disparate exposure in ranking: A learning to rank approach
Meike Zehlike and Carlos Castillo · 2020
Later among the works it cites.
When fair ranking meets uncertain inference
Avijit Ghosh, Ritam Dutt, and Christo Wilson · 2021
Closest in time.
Pairrank: Online pairwise learning to rank by divide-and-conquer
Yiling Jia, Huazheng Wang, Stephen Guo, and Hongning Wang · 2021
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A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan · 2021
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Fairness in ranking under uncertainty
Ashudeep Singh, David Kempe, and Thorsten Joachims · 2021
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Policy-gradient training of fair and unbiased ranking functions
Himank Yadav, Zhengxiao Du, and Thorsten Joachims · 2021
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Meike Zehlike, Ke Yang, and Julia Stoyanovich · 2021
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