Fetching the paper…
Reading the bibliography…
Recommender systems are one of the most pervasive applications of machine learning in industry, with many services using them to match users to products or information.
Optimizing search engines using clickthrough data
T. Joachims · 2002
Earlier work this paper cites.
Learning to rank: from pairwise approach to listwise approach
Z. Cao, T. Qin, T.-Y. Liu, M.-F. Tsai, and H. Li · 2007
Earlier work this paper cites.
Collaborative filtering with temporal dynamics
Y. Koren · 2009
Earlier work this paper cites.
Matrix factorization techniques for recommender systems
Y. Koren, R. Bell, and C. Volinsky · 2009
Earlier work this paper cites.
Three naive bayes approaches for discrimination-free classification
T. Calders and S. Verwer · 2010
Earlier work this paper cites.
Ranking via sinkhorn propagation
R. P. Adams and R. S. Zemel · 2011
Earlier work this paper cites.
Fairness-aware learning through regularization approach
T. Kamishima, S. Akaho, and J. Sakuma · 2011
Earlier work this paper cites.
A cascade ranking model for efficient ranked retrieval
L. Wang, J. Lin, and D. Metzler · 2011
Earlier work this paper cites.
Fairness through awareness
C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel · 2012
Earlier work this paper cites.
Ad click prediction: a view from the trenches
H. B. McMahan, G. Holt, D. Sculley, M. Young, D. Ebner, J. Grady, L. Nie, T. Phillips, E. Davydov, D. Golovin, et al · 2013
Earlier work this paper cites.
Learning fair representations
R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork · 2013
Earlier work this paper cites.
Practical lessons from predicting clicks on ads at facebook
X. He, J. Pan, O. Jin, T. Xu, B. Liu, T. Xu, Y. Shi, A. Atallah, R. Herbrich, S. Bowers, et al · 2014
Earlier work this paper cites.
Beyond clicks: dwell time for personalization
X. Yi, L. Hong, E. Zhong, N. N. Liu, and S. Rajan · 2014
Earlier work this paper cites.
Exposure to ideologically diverse news and opinion on facebook
E. Bakshy, S. Messing, and L. A. Adamic · 2015
Earlier work this paper cites.
Censoring representations with an adversary
H. Edwards and A. Storkey · 2015
Earlier work this paper cites.
Session-based recommendations with recurrent neural networks
B. Hidasi, A. Karatzoglou, L. Baltrunas, and D. Tikk · 2015
Earlier work this paper cites.
The variational fair autoencoder
C. Louizos, K. Swersky, Y. Li, M. Welling, and R. Zemel · 2015
Earlier work this paper cites.
Fairness constraints: Mechanisms for fair classification
M. B. Zafar, I. Valera, M. G. Rodriguez, and K. P. Gummadi · 2015
Cited alongside, same era.
Deep neural networks for youtube recommendations
P. Covington, J. Adams, and E. Sargin · 2016
Cited alongside, same era.
Assessing calibration of prognostic risk scores
C. S. Crowson, E. J. Atkinson, and T. M. Therneau · 2016
Cited alongside, same era.
Satisfying real-world goals with dataset constraints
G. Goh, A. Cotter, M. R. Gupta, and M. P. Friedlander · 2016
Cited alongside, same era.
Equality of opportunity in supervised learning
M. Hardt, E. Price, and N. Srebro · 2016
Cited alongside, same era.
Inherent trade-offs in the fair determination of risk scores
Equity of attention: Amortizing individual fairness in rankings
A. J. Biega, K. P. Gummadi, and G. Weikum · 2018
Later among the works it cites.
Top-k off-policy correction for a reinforce recommender system
M. Chen, A. Beutel, P. Covington, S. Jain, F. Belletti, and E. Chi · 2018
Later among the works it cites.
Measuring and mitigating unintended bias in text classification
L. Dixon, J. Li, J. Sorensen, N. Thain, and L. Vasserman · 2018
Later among the works it cites.
Exploring author gender in book rating and recommendation
M. D. Ekstrand, M. Tian, M. R. I. Kazi, H. Mehrpouyan, and D. Kluver · 2018
Later among the works it cites.
Selection problems in the presence of implicit bias
J. Kleinberg and M. Raghavan · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. Kleinberg, S. Mullainathan, and M. Raghavan · 2016
Cited alongside, same era.
Unbiased comparative evaluation of ranking functions
T. Schnabel, A. Swaminathan, P. I. Frazier, and T. Joachims · 2016
Cited alongside, same era.
Penalizing unfairness in binary classification
Y. Bechavod and K. Ligett · 2017
Cited alongside, same era.
Neural collaborative filtering
X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T.-S. Chua · 2017
Cited alongside, same era.
Unbiased learning-to-rank with biased feedback
T. Joachims, A. Swaminathan, and T. Schnabel · 2017
Cited alongside, same era.
On fairness and calibration
G. Pleiss, M. Raghavan, F. Wu, J. Kleinberg, and K. Q. Weinberger · 2017
Cited alongside, same era.
On conditional parity as a notion of non-discrimination in machine learning
Y. Ritov, Y. Sun, and R. Zhao · 2017
Cited alongside, same era.
Modeling task relationships in multi-task learning with multi-gate mixture-of-experts
J. Ma, Z. Zhao, X. Yi, J. Chen, L. Hong, and E. H. Chi · 2018
Later among the works it cites.
Learning adversarially fair and transferable representations
D. Madras, E. Creager, T. Pitassi, and R. Zemel · 2018
Later among the works it cites.
Towards a fair marketplace: Counterfactual evaluation of the trade-off between relevance, fairness & satisfaction in recommendation systems
R. Mehrotra, J. McInerney, H. Bouchard, M. Lalmas, and F. Diaz · 2018
Later among the works it cites.
Fairness of exposure in rankings
A. Singh and T. Joachims · 2018
Later among the works it cites.
Online set selection with fairness and diversity constraints
J. Stoyanovich, K. Yang, and H. Jagadish · 2018
Later among the works it cites.
Mitigating unwanted biases with adversarial learning
B. H. Zhang, B. Lemoine, and M. Mitchell · 2018
Later among the works it cites.
Fairness-aware tensor-based recommendation
Z. Zhu, X. Hu, and J. Caverlee · 2018
Later among the works it cites.
Putting fairness principles into practice: Challenges, metrics, and improvements
A. Beutel, J. Chen, T. Doshi, H. Qian, A. Woodruff, C. Luu, P. Kreitmann, J. Bischof, and E. H. Chi · 2019
Closest in time.
Nuanced metrics for measuring unintended bias with real data for text classification
D. Borkan, L. Dixon, J. Sorensen, N. Thain, and L. Vasserman · 2019
Closest in time.
Degenerate feedback loops in recommender systems
R. Jiang, S. Chiappa, T. Lattimore, A. Agyorgy, and P. Kohli · 2019
Closest in time.
Policy Learning for Fairness in Ranking
A. Singh and T. Joachims · 2019
Closest in time.