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Conventional Learning-to-Rank (LTR) methods optimize the utility of the rankings to the users, but they are oblivious to their impact on the ranked items.
Individual choice behavior
R Duncan Luce · 1959
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The analysis of permutations
Robin L Plackett · 1975
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The probability ranking principle in ir
Stephen E Robertson · 1977
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Function optimization using connectionist reinforcement learning algorithms
Ronald J Williams and Jing Peng · 1991
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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Introduction to reinforcement learning , volume 135
Richard S Sutton · 1998
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Shaping the web: Why the politics of search engines matters
Lucas D Introna and Helen Nissenbaum · 2000
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Beyond independent relevance: Methods and evaluation metrics for subtopic retrieval
Cheng Xiang Zhai, William W. Cohen, and John Lafferty · 2003
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Learning to rank using gradient descent
Chris Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Greg Hullender · 2005
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Training linear svms in linear time
Thorsten Joachims · 2006
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Learning to rank: from pairwise approach to listwise approach
Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, and Hang Li · 2007
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Evaluating the accuracy of implicit feedback from clicks and query reformulations in web search
Thorsten Joachims, Laura Granka, Bing Pan, Helene Hembrooke, Filip Radlinski, and Geri Gay · 2007
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Softrank: Optimizing non-smooth rank metrics
Michael Taylor, John Guiver, Stephen Robertson, and Tom Minka · 2008
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Building classifiers with independency constraints
Toon Calders, Faisal Kamiran, and Mykola Pechenizkiy · 2009
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Cutting-plane training of structural svms
Thorsten Joachims, Thomas Finley, and Chun-Nam John Yu · 2009
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Stochastic gradient boosted distributed decision trees
Jerry Ye, Jyh-Herng Chow, Jiang Chen, and Zhaohui Zheng · 2009
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Yahoo! learning to rank challenge overview
Olivier Chapelle and Yi Chang · 2011
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Some skepticism about search neutrality
James Grimmelmann · 2011
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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Learning fair representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
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Racial discrimination in the sharing economy: Evidence from a field experiment
Benjamin Edelman, Michael Luca, and Dan Svirsky · 2017
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Unbiased learning-to-rank with biased feedback
T. Joachims, A. Swaminathan, and T. Schnabel · 2017
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Avoiding discrimination through causal reasoning
Niki Kilbertus, Mateo Rojas Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, and Bernhard Schölkopf · 2017
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Counterfactual fairness
Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva · 2017
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Google Fined Record $2.7 Billion in E.U. Antitrust Ruling
Mark Scott · 2017
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Measuring fairness in ranked outputs
Ke Yang and Julia Stoyanovich · 2017
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Matthew Kay, Cynthia Matuszek, and Sean Munson · 2015
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On the relation between accuracy and fairness in binary classification
Indre Zliobaite · 2015
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Big data’s disparate impact
Solon Barocas and Andrew D Selbst · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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Designing fair ranking schemes
Abolfazl Asudehy, HV Jagadishy, Julia Stoyanovichz, and Gautam Das · 2017
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Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi · 2017
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FA* IR: A Fair Top-k Ranking Algorithm
Meike Zehlike, Francesco Bonchi, Carlos Castillo, Sara Hajian, Mohamed Megahed, and Ricardo Baeza-Yates · 2017
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Equity of attention: Amortizing individual fairness in rankings
Asia J. Biega, Krishna P. Gummadi, and Gerhard Weikum · 2018
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Towards a fair marketplace: Counterfactual evaluation of the trade-off between relevance, fairness & satisfaction in recommendation systems
Rishabh Mehrotra, James McInerney, Hugues Bouchard, Mounia Lalmas, and Fernando Diaz · 2018
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Fairness of exposure in rankings
Ashudeep Singh and Thorsten Joachims · 2018
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Reducing disparate exposure in ranking: A learning to rank approach
Meike Zehlike and Carlos Castillo · 2018
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Estimating position bias without intrusive interventions
Aman Agarwal, Ivan Zaitsev, Xuanhui Wang, Cheng Li, Marc Najork, and Thorsten Joachims · 2019
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Intervention harvesting for context-dependent examination-bias estimation
Zhichong Fang, A. Agarwal, and T. Joachims · 2019
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