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We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data.
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Job preferences, college major, and the gender gap in earnings
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Discrimination-aware data mining
D. Pedreshi, S. Ruggieri, and F. Turini · 2008
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Matrix factorization techniques for recommender systems
Y. Koren, R. Bell, and C. Volinsky · 2009
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Collaborative prediction and ranking with non-random missing data
B. M. Marlin and R. S. Zemel · 2009
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Recommendation in higher education using data mining techniques
C. V. Sacin, J. B. Agapito, L. Shafti, and A. Ortigosa · 2009
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Who watches what? Assessing the impact of gender and personality on film preferences
O. Chausson · 2010
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Persistence of women and minorities in STEM field majors: Is it the school that matters?
A. L. Griffith · 2010
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Recommender system for predicting student performance
N. Thai-Nghe, L. Drumond, A. Krohn-Grimberghe, and L. Schmidt-Thieme · 2010
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Women in STEM: A gender gap to innovation
D. N. Beede, T. A. Julian, D. Langdon, G. McKittrick, B. Khan, and M. E. Doms · 2011
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Collaborative filtering recommender systems
M. D. Ekstrand, J. T. Riedl, J. A. Konstan, et al · 2011
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Fairness-aware learning through regularization approach
T. Kamishima, S. Akaho, and J. Sakuma · 2011
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Women into science and engineering? Gendered participation in higher education STEM subjects
E. Smith · 2011
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Enhancement of the neutrality in recommendation
T. Kamishima, S. Akaho, H. Asoh, and J. Sakuma · 2012
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Collaborative filtering and the missing at random assumption
B. Marlin, R. S. Zemel, S. Roweis, and M. Slaney · 2012
Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2014
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It takes two to tango: An exploration of domain pairs for cross-domain collaborative filtering
S. Sahebi and P. Brusilovsky · 2015
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Educational recommender systems and their application in lifelong learning
M.-I. Dascalu, C.-N. Bodea, M. N. Mihailescu, E. A. Tanase, and P. Ordoñez de Pablos · 2016
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Equality of opportunity in supervised learning
M. Hardt, E. Price, N. Srebro, et al · 2016
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The Movielens datasets: History and context
F. M. Harper and J. A. Konstan · 2016
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Model-based approaches for independence-enhanced recommendation
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Efficiency improvement of neutrality-enhanced recommendation
T. Kamishima, S. Akaho, H. Asoh, and J. Sakuma · 2013
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R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork · 2013
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Recruiting women into computer science and information systems
S. Broad and M. McGee · 2014
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Correcting popularity bias by enhancing recommendation neutrality
T. Kamishima, S. Akaho, H. Asoh, and J. Sakuma · 2014
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T. Kamishima, S. Akaho, H. Asoh, and I. Sato · 2016
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A statistical framework for fair predictive algorithms
K. Lum and J. Johndrow · 2016
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Beyond globally optimal: Focused learning for improved recommendations
A. Beutel, E. H. Chi, Z. Cheng, H. Pham, and J. Anderson · 2017
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Fairness constraints: Mechanisms for fair classification
M. B. Zafar, I. Valera, M. Gomez Rodriguez, and K. P. Gummadi · 2017
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