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Recommender systems are being employed across an increasingly diverse set of domains that can potentially make a significant social and individual impact.
Algorithms for non-negative matrix factorization
Daniel D Lee and H Sebastian Seung · 2001
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Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky · 2009
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Fairness-aware classifier with prejudice remover regularizer
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma · 2012
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The movielens datasets: History and context
F Maxwell Harper and Joseph A Konstan · 2015
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Hyper: A flexible and extensible probabilistic framework for hybrid recommender systems
Pigi Kouki, Shobeir Fakhraei, James Foulds, Magdalini Eirinaki, and Lise Getoor · 2015
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nathan Srebro · 2016
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Algorithmic bias? an empirical study into apparent gender-based discrimination in the display of stem career ads
Anja Lambrecht and Catherine Tucker · 2016
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The variational fair autoencoder
Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, and Richard S. Zemel · 2016
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Hinge-loss markov random fields and probabilistic soft logic
Stephen H. Bach, Matthias Broecheler, Bert Huang, and Lise Getoor · 2017
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Data decisions and theoretical implications when adversarially learning fair representations
Alex Beutel, Jilin Chen, Zhe Zhao, and Ed Chi · 2017
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Optimized pre-processing for discrimination prevention
Flavio Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, and Kush R Varshney · 2017
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User preferences for hybrid explanations
Pigi Kouki, James Schaffer, Jay Pujara, John ODonovan, and Lise Getoor · 2017
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Beyond parity: Fairness objectives for collaborative filtering
Sirui Yao and Bert Huang · 2017
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Men also like shopping: Reducing gender bias amplification using corpus-level constraints
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang · 2017
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Fairness in relational domains
Golnoosh Farnadi, Behrouz Babaki, and Lise Getoor · 2018
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A fairness-aware hybrid recommender system
Golnoosh Farnadi, Pigi Kouki, Spencer K. Thompson, Sriram Srinivasan, and Lise Getoor · 2018
Fairness in recommendation ranking through pairwise comparisons
Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, and Cristos Goodrow · 2019
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All the cool kids, how do they fit in?: Popularity and demographic biases in recommender evaluation and effectiveness
Michael D. Ekstrand, Mucun Tian, Ion Madrazo Azpiazu, Jennifer D. Ekstrand, Oghenemaro Anuyah, David McNeill, and Maria Soledad Pera · 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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ifair: Learning individually fair data representations for algorithmic decision making
Preethi Lahoti, Krishna P Gummadi, and Gerhard Weikum · 2019
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Personalized fairness-aware re-ranking for microlending
Weiwen Liu, Jun Guo, Nasim Sonboli, Robin Burke, and Shengyu Zhang · 2019
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Assessing and addressing algorithmic bias - but before we get there
Jean Garcia-Gathright, Aaron Springer, and Henriette Cramer · 2018
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Fairness of exposure in rankings
Ashudeep Singh and Thorsten Joachims · 2018
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Discrimination through optimization: How facebook’s ad delivery can lead to biased outcomes
Muhammad Ali, Piotr Sapiezynski, Miranda Bogen, Aleksandra Korolova, Alan Mislove, and Aaron Rieke · 2019
Cited alongside, same era.
Learning adversarially fair and transferable representations
David Madras, Elliot Creager, Toniann Pitassi, and Richard S. Zemel · 2019
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A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan · 2019
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Matchmaking under fairness constraints: a speed dating case study
Dimitris Paraschakis and Bengt Nilsson · 2020
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Tandem inference: An out-of-core streaming algorithm for very large-scale relational inference
Sriram Srinivasan, Eriq Augustine, and Lise Getoor · 2020
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