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We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data.
Local structure in social networks
Paul W. Holland and Samuel Leinhardt · 1976
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Job preferences, college major, and the gender gap in earnings
Thomas N. Daymont and Paul J. Andrisani · 1984
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Discrimination-aware data mining
Dino Pedreshi, Salvatore Ruggieri, and Franco Turini · 2008
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Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky · 2009
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Collaborative prediction and ranking with non-random missing data
Benjamin M. Marlin and Richard S. Zemel · 2009
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Recommendation in higher education using data mining techniques
Cesar Vialardi Sacin, Javier Bravo Agapito, Leila Shafti, and Alvaro Ortigosa · 2009
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Who watches what? Assessing the impact of gender and personality on film preferences
Olivia Chausson · 2010
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Persistence of women and minorities in STEM field majors: Is it the school that matters?
Amanda L. Griffith · 2010
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Recommender system for predicting student performance
Nguyen Thai-Nghe, Lucas Drumond, Artus Krohn-Grimberghe, and Lars Schmidt-Thieme · 2010
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Women in STEM: A gender gap to innovation
David N Beede, Tiffany A Julian, David Langdon, George McKittrick, Beethika Khan, and Mark E Doms · 2011
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Fairness-aware learning through regularization approach
Toshihiro Kamishima, Shotaro Akaho, and Jun Sakuma · 2011
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Women into science and engineering? Gendered participation in higher education STEM subjects
Emma Smith · 2011
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Enhancement of the neutrality in recommendation
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma · 2012
Cited alongside, same era.
Collaborative filtering and the missing at random assumption
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Educational recommender systems and their application in lifelong learning
Maria-Iuliana Dascalu, Constanta-Nicoleta Bodea, Monica Nastasia Mihailescu, Elena Alice Tanase, and Patricia Ordoñez de Pablos · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, Nati Srebro, et al · 2016
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The Movielens datasets: History and context
F Maxwell Harper and Joseph A Konstan · 2016
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Benjamin Marlin, Richard S Zemel, Sam Roweis, and Malcolm Slaney · 2012
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Controlling attribute effect in linear regression
Toon Calders, Asim Karim, Faisal Kamiran, Wasif Ali, and Xiangliang Zhang · 2013
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Efficiency improvement of neutrality-enhanced recommendation
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma · 2013
Cited alongside, same era.
Kristian Lum and James Johndrow · 2016
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Fairness constraints: Mechanisms for fair classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P. Gummadi · 2017
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