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Ranking systems are the key components of modern Information Retrieval (IR) applications, such as search engines and recommender systems.
Inequalities
Godfrey Harold Hardy, John Edensor Littlewood, George Pólya, György Pólya, et al · 1952
Earlier work this paper cites.
The probability ranking principle in IR
Stephen E Robertson. 1977 · 1977
Earlier work this paper cites.
Minimally invasive randomization for collecting unbiased preferences from clickthrough logs. In Proceedings of the national conference on artificial intelligence , Vol. 21. Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999, 1406
Filip Radlinski and Thorsten Joachims. 2006 · 1999
Earlier work this paper cites.
Cumulated gain-based evaluation of IR techniques
Kalervo Järvelin and Jaana Kekäläinen. 2002 · 2002
Earlier work this paper cites.
Predicting clicks: estimating the click-through rate for new ads. In Proceedings of the 16th international conference on World Wide Web . 521–530
Matthew Richardson, Ewa Dominowska, and Robert Ragno. 2007 · 2007
Earlier work this paper cites.
A comparison of statistical significance tests for information retrieval evaluation. In Proceedings of the sixteenth ACM conference on Conference on information and knowledge management . 623–632
Mark D Smucker, James Allan, and Ben Carterette. 2007 · 2007
Earlier work this paper cites.
An experimental comparison of click position-bias models. In Proceedings of the 2008 international conference on web search and data mining . 87–94
Nick Craswell, Onno Zoeter, Michael Taylor, and Bill Ramsey. 2008 · 2008
Earlier work this paper cites.
Learning to rank for information retrieval
Tie-Yan Liu et al · 2009
Earlier work this paper cites.
Introducing LETOR 4.0 datasets
Tao Qin and Tie-Yan Liu. 2013 · 2013
Earlier work this paper cites.
Post-learning optimization of tree ensembles for efficient ranking. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval . 949–952
Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Fabrizio Silvestri, and Salvatore Trani. 2016 · 2016
Earlier work this paper cites.
Ranking with fairness constraints
L Elisa Celis, Damian Straszak, and Nisheeth K Vishnoi. 2017 · 2017
Earlier work this paper cites.
Accurately interpreting clickthrough data as implicit feedback. In ACM SIGIR Forum , Vol. 51. Acm New York, NY, USA, 4–11
Thorsten Joachims, Laura Granka, Bing Pan, Helene Hembrooke, and Geri Gay. 2017 · 2017
Earlier work this paper cites.
Fa* ir: A fair top-k ranking algorithm. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management . 1569–1578
Meike Zehlike, Francesco Bonchi, Carlos Castillo, Sara Hajian, Mohamed Megahed, and Ricardo Baeza-Yates. 2017 · 2017
Earlier work this paper cites.
Unbiased learning to rank with unbiased propensity estimation. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval . 385–394
Qingyao Ai, Keping Bi, Cheng Luo, Jiafeng Guo, and W Bruce Croft. 2018 · 2018
Earlier work this paper cites.
Equity of attention: Amortizing individual fairness in rankings. In The 41st international acm sigir conference on research & development in information retrieval . 405–414
Asia J Biega, Krishna P Gummadi, and Gerhard Weikum. 2018 · 2018
Earlier work this paper cites.
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
Earlier work this paper cites.
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
Earlier work this paper cites.
Designing fair ranking schemes. In Proceedings of the 2019 international conference on management of data . 1259–1276
Abolfazl Asudeh, HV Jagadish, Julia Stoyanovich, and Gautam Das. 2019 · 2019
Cited alongside, same era.
Fairness-aware ranking in search & recommendation systems with application to linkedin talent search. In Proceedings of the 25th acm sigkdd international conference on knowledge discovery & data mining . 2221–2231
Sahin Cem Geyik, Stuart Ambler, and Krishnaram Kenthapadi. 2019 · 2019
Cited alongside, same era.
Policy learning for fairness in ranking. In Advances in Neural Information Processing Systems . 5426–5436
Ashudeep Singh and Thorsten Joachims. 2019 · 2019
Cited alongside, same era.
Variance reduction in gradient exploration for online learning to rank. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval . 835–844
Huazheng Wang, Sonwoo Kim, Eric McCord-Snook, Qingyun Wu, and Hongning Wang. 2019 · 2019
Cited alongside, same era.
Meike Zehlike, Ke Yang, and Julia Stoyanovich. 2021 · 2021
Later among the works it cites.
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
Later among the works it cites.
FAIR: Fairness-aware information retrieval evaluation
Ruoyuan Gao, Yingqiang Ge, and Chirag Shah. 2022 · 2022
Later among the works it cites.
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
Later among the works it cites.
Fairness of Exposure in Light of Incomplete Exposure Estimation
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Evaluating stochastic rankings with expected exposure. In Proceedings of the 29th ACM international conference on information & knowledge management . 275–284
Fernando Diaz, Bhaskar Mitra, Michael D Ekstrand, Asia J Biega, and Ben Carterette. 2020 · 2020
Cited alongside, same era.
Controlling Fairness and Bias in Dynamic Learning-to-Rank. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (Virtual Event, China) (SIGIR ’20) . Association for Computing Machinery, New York, NY, USA, 429–438
Marco Morik, Ashudeep Singh, Jessica Hong, and Thorsten Joachims. 2020 · 2020
Cited alongside, same era.
Policy-aware unbiased learning to rank for top-k rankings. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval . 489–498
Harrie Oosterhuis and Maarten de Rijke. 2020 · 2020
Cited alongside, same era.
Reducing disparate exposure in ranking: A learning to rank approach. In Proceedings of The Web Conference 2020 . 2849–2855
Meike Zehlike and Carlos Castillo. 2020 · 2020
Cited alongside, same era.
Addressing bias and fairness in search systems. In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval . 2643–2646
Ruoyuan Gao and Chirag Shah. 2021 · 2021
Cited alongside, same era.
Fairness and Control of Exposure in Two-sided Markets. In Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval . 1–1
Thorsten Joachims. 2021 · 2021
Cited alongside, same era.
Towards personalized fairness based on causal notion. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . 1054–1063
Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, and Yongfeng Zhang. 2021 · 2021
Cited alongside, same era.
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
Harrie Oosterhuis. 2021 · 2021
Cited alongside, same era.
Maria Heuss, Fatemeh Sarvi, and Maarten de Rijke. 2022 · 2022
Later among the works it cites.
End-to-End Learning for Fair Ranking Systems. In Proceedings of the ACM Web Conference 2022 . 3520–3530
James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck, and Ziwei Zhu. 2022 · 2022
Later among the works it cites.
Understanding and mitigating multi-sided exposure bias in recommender systems
Masoud Mansoury. 2022 · 2022
Later among the works it cites.
Cpfair: Personalized consumer and producer fairness re-ranking for recommender systems
Mohammadmehdi Naghiaei, Hossein A Rahmani, and Yashar Deldjoo. 2022 · 2022
Later among the works it cites.
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
Later among the works it cites.
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
Later among the works it cites.
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
Later among the works it cites.
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
Later among the works it cites.
Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in Ranking
Ali Vardasbi, Fatemeh Sarvi, and Maarten de Rijke. 2022 · 2022
Later among the works it cites.
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
Later among the works it cites.
Effective Exposure Amortizing for Fair Top-k Recommendation
Tao Yang, Zhichao Xu, and Qingyao Ai. 2022b · 2022
Later among the works it cites.
Overview of the TREC 2022 Fair Ranking Track
Michael D Ekstrand, Graham McDonald, Amifa Raj, and Isaac Johnson. 2023 · 2023
Closest in time.
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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