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Most recommender systems (RS) research assumes that a user's utility can be maximized independently of the utility of the other agents (e.g., other users, content providers).
Recsim: A configurable simulation platform for recommender systems
Ie, E., Hsu, C.-w., Mladenov, M., Jain, V., Narvekar, S., Wang, J., Wu, R., and Boutilier, C · 1909
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Distributed optimization and statistical learning via the alternating direction method of multipliers
Boyd, S., Parikh, N., Chu, E., Peleato, B., and Eckstein, J · 1935
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Location of bank accounts to optimize float: An analytic study of exact and approximate algorithms
Cornuejols, G., Fisher, M. L., and Nemhauser, G. L · 1977
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Prospect theory: An analysis of decision under risk
Kahneman, D. and Tversky, A · 1979
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GroupLens: Applying collaborative filtering to usenet news
Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R., and Riedl, J · 1997
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The facility location problem with general cost functions
Hajiaghayi, M. T., Mahdian, M., and Mirrokni, V. S · 2003
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Probabilistic matrix factorization
Salakhutdinov, R. and Mnih, A · 2007
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Collaborative filtering for implicit feedback datasets
Hu, Y., Koren, Y., and Volinsky, C · 2008
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Optimizing long-term social welfare in recommender systems: A constrained matching approach
Mladenov, M., Creager, E., Ben-Porat, O., Swersky, K., Zemel, R., and Boutilier, C · 2008
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The long tail in recommender systems
Celma, Ò · 2010
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What is Twitter, a social network or a news media?
Kwak, H., Lee, C., Park, H., and Moon, S · 2010
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Fairness through awareness
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R · 2012
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Online matching and ad allocation
Mehta, A · 2013
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The Movielens datasets: History and context
Harper, F. M. and Konstan, J. A · 2015
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The maximum facility location problem
Jones, M · 2015
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Big data’s disparate impact
Barocas, S. and Selbst, A. D · 2016
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Deep neural networks for YouTube recommendations
Covington, P., Adams, J., and Sargin, E · 2016
Cited alongside, same era.
Music personalization at Spotify
Jacobson, K., Murali, V., Newett, E., Whitman, B., and Yon, R · 2016
Cited alongside, same era.
Fairness in learning: Classic and contextual bandits
Joseph, M., Kearns, M., Morgenstern, J. H., and Roth, A · 2016
Cited alongside, same era.
To predict and serve?
Lum, K. and Isaac, W · 2016
Cited alongside, same era.
Distributed submodular maximization
Mirzasoleiman, B., Karbasi, A., Sarkar, R., and Krause, A · 2016
Cited alongside, same era.
Measuring fairness in ranked outputs
Yang, K. and Stoyanovich, J · 2016
Cited alongside, same era.
LP-based algorithms for capacitated facility location
How algorithmic confounding in recommendation systems increases homogeneity and decreases utility
Chaney, A. J. B., Stewart, B. M., and Engelhardt, B. E · 2018
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Top-k off-policy correction for a REINFORCE recommender system
Chen, M., Beutel, A., Covington, P., Jain, S., Belletti, F., and Chi, E · 2018
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Runaway feedback loops in predictive policing
Ensign, D., Friedler, S. A., Neville, S., Scheidegger, C., and Venkatasubramanian, S · 2018
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Fairness without demographics in repeated loss minimization
Hashimoto, T. B., Srivastava, M., Namkoong, H., and Liang, P · 2018
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A short-term intervention for long-term fairness in the labor market
Hu, L. and Chen, Y · 2018
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Fairness of exposure in rankings
Singh, A. and Joachims, T · 2018
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An, H., Singh, M., and Svensson, O · 2017
Cited alongside, same era.
Ranking with fairness constraints
Celis, L. E., Straszak, D., and Vishnoi, N. K · 2017
Cited alongside, same era.
Importance sampling for fair policy selection
Doroudi, S., Thomas, P. S., and Brunskill, E · 2017
Cited alongside, same era.
Neural collaborative filtering
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., and Chua, T · 2017
Cited alongside, same era.
Fairness in reinforcement learning
Jabbari, S., Joseph, M., Kearns, M., Morgenstern, J., and Roth, A · 2017
Cited alongside, same era.
Fa*ir: A fair top-k ranking algorithm
Zehlike, M., Bonchi, F., Castillo, C., Hajian, S., Megahed, M., and Baeza-Yates, R · 2017
Cited alongside, same era.
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Designing fair ranking schemes
Asudeh, A., Jagadishy, H., Stoyanovichz, J., and Das, G · 2019
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From recommendation systems to facility location games
Ben-Porat, O., Goren, G., Rosenberg, I., and Tennenholtz, M · 2019
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Fairness in recommendation ranking through pairwise comparisons
Beutel, A., Chen, J., Doshi, T., Qian, H., Wei, L., Wu, Y., Heldt, L., Zhao, Z., Hong, L., Chi, E. H., and Goodrow, C · 2019
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SIREN: A simulation framework for understanding the effects of recommender systems in online news environments
Bountouridis, D., Harambam, J., Makhortykh, M., Marrero, M., Tintarev, N., and Hauff, C · 2019
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The disparate effects of strategic manipulation
Hu, L., Immorlica, N., and Vaughan, J. W · 2019
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Downstream effects of affirmative action
Kannan, S., Roth, A., and Ziani, J · 2019
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On facility location with general lower bounds
Li, S · 2019
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From fair decision making to social qquality
Mouzannar, H., Ohannessian, M. I., and Srebro, N · 2019
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Normative principles for evaluating fairness in machine learning
Leben, D · 2020
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Auditing radicalization pathways on YouTube
Ribeiro, M. H., Ottoni, R., West, R., Almeida, V. A. F., and Jr., W. M · 2020
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