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Recommender systems are facing scrutiny because of their growing impact on the opportunities we have access to.
A framework for understanding unintended consequences of machine learning
Suresh, H.; and Guttag, J. V. 2019 · 1901
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An algorithm for quadratic programming
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Probability Inequalities for Sums of Bounded Random Variables
Hoeffding, W. 1963 · 1963
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Resource allocation and the public sector
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Freedman, D. A. 1975 · 1975
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An information-maximization approach to blind separation and blind deconvolution
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The possibility of social choice
Sen, A. 1999 · 1999
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Hybrid recommender systems: Survey and experiments
Burke, R. 2002 · 2002
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Learning with kernels: support vector machines, regularization, optimization, and beyond
Schölkopf, B.; and Smola, A. J. 2002 · 2002
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Tutorial on Practical Prediction Theory for Classification
Langford, J. 2005 · 2005
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Collaborative filtering for implicit feedback datasets
Hu, Y.; Koren, Y.; and Volinsky, C. 2008 · 2008
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Pure exploration in multi-armed bandits problems
Bubeck, S.; Munos, R.; Stoltz, G.; and . 2009 · 2009
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Matrix factorization techniques for recommender systems
Koren, Y.; Bell, R.; and Volinsky, C. 2009 · 2009
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Best arm identification in multi-armed bandits
Audibert, J.-Y.; and Bubeck, S. 2010 · 2010
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2nd Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec 2011)
Cantador, I.; Brusilovsky, P.; Kuflik, T.; and . 2011 · 2011
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Fairness through awareness
Dwork, C.; Hardt, M.; Pitassi, T.; Reingold, O.; and Zemel, R. 2012 · 2012
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Discrimination in online ad delivery
Sweeney, L. 2013 · 2013
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Measuring price discrimination and steering on e-commerce web sites
Hannak, A.; Soeller, G.; Lazer, D.; Mislove, A.; and Wilson, C. 2014 · 2014
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lil’ucb: An optimal exploration algorithm for multi-armed bandits
Jamieson, K.; Malloy, M.; Nowak, R.; and Bubeck, S. 2014 · 2014
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Best-arm identification algorithms for multi-armed bandits in the fixed confidence setting
Jamieson, K.; and Nowak, R. 2014 · 2014
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Logistic matrix factorization for implicit feedback data
Johnson, C. C. 2014 · 2014
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Automated experiments on ad privacy settings
Datta, A.; Tschantz, M. C.; Datta, A.; and . 2015 · 2015
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The movielens datasets: History and context
Harper, F. M.; and Konstan, J. A. 2015 · 2015
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Balcan, M.-F.; Dick, T.; Noothigattu, R.; and Procaccia, A. D. 2018 · 2018
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Fairness and Machine Learning
Barocas, S.; Hardt, M.; and Narayanan, A. 2018 · 2018
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Equity of attention: Amortizing individual fairness in rankings
Biega, A. J.; Gummadi, K. P.; Weikum, G.; and . 2018 · 2018
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The measure and mismeasure of fairness: A critical review of fair machine learning
Corbett-Davies, S.; and Goel, S. 2018 · 2018
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All the cool kids, how do they fit in?: Popularity and demographic biases in recommender evaluation and effectiveness
Ekstrand, M. D.; Tian, M.; Azpiazu, I. M.; Ekstrand, J. D.; Anuyah, O.; McNeill, D.; and Pera, M. S. 2018 · 2018
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Big Data’s Disparate Impact
Barocas, S.; and Selbst, A. D. 2016 · 2016
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Fairness in learning: Classic and contextual bandits
Joseph, M.; Kearns, M.; Morgenstern, J. H.; and Roth, A. 2016 · 2016
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An optimal algorithm for the thresholding bandit problem
Locatelli, A.; Gutzeit, M.; and Carpentier, A. 2016 · 2016
Cited alongside, same era.
Conservative bandits
Wu, Y.; Shariff, R.; Lattimore, T.; and Szepesvári, C. 2016 · 2016
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Multisided fairness for recommendation
Burke, R. 2017 · 2017
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Ranking with fairness constraints
Celis, L. E.; Straszak, D.; and Vishnoi, N. K. 2017 · 2017
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Fairness of exposure in rankings
Singh, A.; and Joachims, T. 2018 · 2018
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Modeling dynamic missingness of implicit feedback for recommendation
Wang, M.; Gong, M.; Zheng, X.; and Zhang, K. 2018 · 2018
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Fairness-aware ranking in search & recommendation systems with application to linkedin talent search
Geyik, S. C.; Ambler, S.; and Kenthapadi, K. 2019 · 2019
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Good arm identification via bandit feedback
Kano, H.; Honda, J.; Sakamaki, K.; Matsuura, K.; Nakamura, A.; and Sugiyama, M. 2019 · 2019
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Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads
Lambrecht, A.; and Tucker, C. 2019 · 2019
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Fairness without harm: Decoupled classifiers with preference guarantees
Ustun, B.; Liu, Y.; Parkes, D.; and . 2019 · 2019
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Auditing race and gender discrimination in online housing markets
Asplund, J.; Eslami, M.; Sundaram, H.; Sandvig, C.; and Karahalios, K. 2020 · 2020
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Improved algorithms for conservative exploration in bandits
Garcelon, E.; Ghavamzadeh, M.; Lazaric, A.; and Pirotta, M. 2020 · 2020
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Multi-category fairness in sponsored search auctions
Ilvento, C.; Jagadeesan, M.; and Chawla, S. 2020 · 2020
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FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms
Patro, G. K.; Biswas, A.; Ganguly, N.; Gummadi, K. P.; and Chakraborty, A. 2020 · 2020
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Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing
Raji, I. D.; Smart, A.; White, R. N.; Mitchell, M.; Gebru, T.; Hutchinson, B.; Smith-Loud, J.; Theron, D.; and Barnes, P. 2020 · 2020
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Auditing for Discrimination in Algorithms Delivering Job Ads
Imana, B.; Korolova, A.; Heidemann, J.; and . 2021 · 2021
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