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Recommender systems are used in variety of domains affecting people's lives.
Discrimination-aware data mining
D. Pedreshi, S. Ruggieri, and F. Turini · 2008
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Building classifiers with independency constraints
T. Calders, F. Kamiran, and M. Pechenizkiy · 2009
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Discrimination aware decision tree learning
F. Kamiran, T. Calders, and M. Pechenizkiy · 2010
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Fairness through awareness
C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel · 2012
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Fairness-aware classifier with prejudice remover regularizer
T. Kamishima, S. Akaho, H. Asoh, and J. Sakuma · 2012
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A study of top-k measures for discrimination discovery
Dino Pedreschi, Salvatore Ruggieri, and Franco Turini · 2012
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Bias in algorithmic filtering and personalization
E. Bozdag · 2013
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Learning fair representations
R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork · 2013
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Knowledge graph identification
J. Pujara, H. Miao, L. Getoor, and W. Cohen · 2013
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Automated experiments on ad privacy settings
A. Datta, Michael C. Tschantz, and A. Datta · 2015
Cited alongside, same era.
Hyper: A flexible and extensible probabilistic framework for hybrid recommender systems
P. Kouki, S. Fakhraei, J. Foulds, M. Eirinaki, and L. Getoor · 2015
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Joint models of disagreement and stance in online debate
D. Sridhar, J. Foulds, M. Walker, B. Huang, and L. Getoor · 2015
Cited alongside, same era.
A comprehensive survey of neighborhood-based recommendation methods
X. Ning, C. Desrosiers, and G. Karypis · 2015
Cited alongside, same era.
The movielens datasets: History and context
M. Harper and J. Konstan · 2015
Cited alongside, same era.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
Cited alongside, same era.
Presenting diversity aware recommendations: Making challenging news acceptable
N. Tintarev · 2017
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Balanced neighborhoods for fairness-aware collaborative recommendation
R. Burke, N. Sonboli, and M. Mansoury · 2017
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Academic performance prediction in a gender-imbalanced environment
P. Sapiezynski, V. Kassarnig, and S. Lehmann · 2017
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Hinge-loss markov random fields and probabilistic soft logic
S. Bach, M. Broecheler, B. Huang, and L. Getoor · 2017
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User preferences for hybrid explanations
P. Kouki, J. Schaffer, J. Pujara, J. O’ Donovan, and L. Getoor · 2017
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Soft quantification in statistical relational learning
G. Farnadi, S. H Bach, M-F. Moens, L. Getoor, and M. De Cock · 2017
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Incorporating diversity in a learning to rank recommender system
J. Wasilewski and N. Hurley · 2016
Cited alongside, same era.
Beyond parity: Fairness objectives for collaborative filtering
S. Yao and B. Huang · 2017
Cited alongside, same era.
Online interactive collaborative filtering using multi-armed bandit with dependent arms
Q. Wang, Ch. Zeng, W. Zhou, T. Li, L. Shwartz, and G. Grabarnik · 2017
Cited alongside, same era.
Disambiguating energy disaggregation: A collective probabilistic approach
S. Tomkins, J. Pujara, and L. Getoor · 2017
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Multisided fairness for recommendation
R. Burke · 2017
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Fairness in relational domains
G. Farnadi, B. Babaki, and L. Getoor · 2018
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