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Result ranking often affects consumer satisfaction as well as the amount of exposure each item receives in the ranking services.
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Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . 1023–1032
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Fairness of exposure in rankings. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 2219–2228
Ashudeep Singh and Thorsten Joachims. 2018 · 2018
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Automated directed fairness testing. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering . 98–108
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Position bias estimation for unbiased learning to rank in personal search. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining . 610–618
Xuanhui Wang, Nadav Golbandi, Michael Bendersky, Donald Metzler, and Marc Najork. 2018 · 2018
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Fairness-aware tensor-based recommendation. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management . 1153–1162
Ziwei Zhu, Xia Hu, and James Caverlee. 2018 · 2018
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Estimating position bias without intrusive interventions. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining . 474–482
Aman Agarwal, Ivan Zaitsev, Xuanhui Wang, Cheng Li, Marc Najork, and Thorsten Joachims. 2019 · 2019
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Fairness in recommendation ranking through pairwise comparisons. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 2212–2220
Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H Chi, et al · 2019
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Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads
Anja Lambrecht and Catherine Tucker. 2019 · 2019
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Policy learning for fairness in ranking. In Advances in Neural Information Processing Systems . 5426–5436
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Tao Yang and Qingyao Ai. 2021 · 2021
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Gender Fairness in Information Retrieval Systems. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval . 3436–3439
Amin Bigdeli, Negar Arabzadeh, Shirin SeyedSalehi, Morteza Zihayat, and Ebrahim Bagheri. 2022 · 2022
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FAIR: Fairness-aware information retrieval evaluation
Ruoyuan Gao, Yingqiang Ge, and Chirag Shah. 2022 · 2022
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Explainable Fairness in Recommendation
Yingqiang Ge, Juntao Tan, Yan Zhu, Yinglong Xia, Jiebo Luo, Shuchang Liu, Zuohui Fu, Shijie Geng, Zelong Li, and Yongfeng Zhang. 2022 · 2022
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Cpfair: Personalized consumer and producer fairness re-ranking for recommender systems
Mohammadmehdi Naghiaei, Hossein A Rahmani, and Yashar Deldjoo. 2022 · 2022
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Fair ranking: a critical review, challenges, and future directions
Gourab K Patro, Lorenzo Porcaro, Laura Mitchell, Qiuyue Zhang, Meike Zehlike, and Nikhil Garg. 2022 · 2022
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Measuring Fairness in Ranked Results: An Analytical and Empirical Comparison. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval . 726–736
Amifa Raj and Michael D Ekstrand. 2022 · 2022
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Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 1514–1524
Yuta Saito and Thorsten Joachims. 2022 · 2022
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Fast online ranking with fairness of exposure. In 2022 ACM Conference on Fairness, Accountability, and Transparency . 2157–2167
Nicolas Usunier, Virginie Do, and Elvis Dohmatob. 2022 · 2022
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Joint multisided exposure fairness for recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval . 703–714
Haolun Wu, Bhaskar Mitra, Chen Ma, Fernando Diaz, and Xue Liu. 2022 · 2022
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Reinforcement Learning to Rank with Coarse-grained Labels
Zhichao Xu, Anh Tran, Tao Yang, and Qingyao Ai. 2022 · 2022
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Can clicks be both labels and features? Unbiased Behavior Feature Collection and Uncertainty-aware Learning to Rank. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval . 6–17
Tao Yang, Chen Luo, Hanqing Lu, Parth Gupta, Bing Yin, and Qingyao Ai. 2022 · 2022
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Overview of the TREC 2022 Fair Ranking Track
Michael D Ekstrand, Graham McDonald, Amifa Raj, and Isaac Johnson. 2023 · 2023
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P-MMF: Provider Max-min Fairness Re-ranking in Recommender System. In Proceedings of the ACM Web Conference 2023 . 3701–3711
Chen Xu, Sirui Chen, Jun Xu, Weiran Shen, Xiao Zhang, Gang Wang, and Zhenhua Dong. 2023 · 2023
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Mitigating Exploitation Bias in Learning to Rank with an Uncertainty-aware Empirical Bayes Approach
Tao Yang, Cuize Han, Chen Luo, Parth Gupta, Jeff M Phillips, and Qingyao Ai. 2023a · 2023
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FARA: Future-aware Ranking Algorithm for Fairness Optimization
Tao Yang, Zhichao Xu, Zhenduo Wang, and Qingyao Ai. 2023b · 2023
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