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Prior research on exposure fairness in the context of recommender systems has focused mostly on disparities in the exposure of individual or groups of items to individual users of the system.
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Cited alongside, same era.
The MovieLens Datasets: History and Context
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Learning to Rank with Selection Bias in Personal Search. In SIGIR . ACM, 115–124
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Robin Burke. 2017 · 2017
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Fairness-Aware Group Recommendation with Pareto-Efficiency. In RecSys . ACM, 107–115
Xiao Lin, Min Zhang, Yongfeng Zhang, Zhaoquan Gu, Yiqun Liu, and Shaoping Ma. 2017 · 2017
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Policy-Gradient Training of Fair and Unbiased Ranking Functions
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Evaluating stochastic rankings with expected exposure. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management . 275–284
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Policy-Aware Unbiased Learning to Rank for Top-k Rankings. In SIGIR . ACM, 489–498
Harrie Oosterhuis and Maarten de Rijke. 2020 · 2020
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FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms. In WWW . ACM / IW3C2, 1194–1204
Gourab K. Patro, Arpita Biswas, Niloy Ganguly, Krishna P. Gummadi, and Abhijnan Chakraborty. 2020 · 2020
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Not All Relevance Scores are Equal: Efficient Uncertainty and Calibration Modeling for Deep Retrieval Models. In Proc. SIGIR . ACM
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The history of the gender wage gap in America
Greg Daugherty. 2021 · 2021
Later among the works it cites.
Fairness and Discrimination in Information Access Systems
Michael D Ekstrand, Anubrata Das, Robin Burke, and Fernando Diaz. 2021 · 2021
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A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. 2021 · 2021
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Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness. In Proc. SIGIR
Harrie Oosterhuis. 2021 · 2021
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Navid Rekabsaz, Simone Kopeinik, and Markus Schedl. 2021 · 2021
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Ward Williams. 2021 · 2021
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Explainable Fairness in Recommendation. In Proc. of SIGIR . ACM
Yingqiang Ge, Juntao Tan, Yan Zhu, Yinglong Xia, Jiebo Luo, Shuchang Liu, Zuohui Fu, Shijie Geng, Zelong Li, and Yongfeng Zhang. 2022 · 2022
Closest in time.
Revisiting Popularity and Demographic Biases in Recommender Evaluation and Effectiveness. In Proc. ECIR
Nicola Neophytou, Bhaskar Mitra, and Catherine Stinson. 2022 · 2022
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