Recent Advances in the Foundations and Applications of Unbiased Learning to Rank
Not on the map. Why?
Abstract (arXiv)
Since its inception, the field of unbiased learning to rank (ULTR) has remained very active and has seen several impactful advancements in recent years. This tutorial provides both an introduction to the core concepts of the field and an overview of recent advancements in its foundations along with several applications of its methods. The tutorial is divided into four parts: Firstly, we give an overview of the different forms of bias that can be addressed with ULTR methods. Secondly, we present a comprehensive discussion of the latest estimation techniques in the ULTR field. Thirdly, we survey published results of ULTR in real-world applications. Fourthly, we discuss the connection between ULTR and fairness in ranking. We end by briefly reflecting on the future of ULTR research and its applications. This tutorial is intended to benefit both researchers and industry practitioners who are interested in developing new ULTR solutions or utilizing them in real-world applications.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 27 of its 59 references that are in the map. First posted on arXiv 4 May 2023.
Builds on
27 of 59 references.
References · 27
- 2015Doubly Robust Off-policy Value Evaluation for Reinforcement Learningcited by 339
- 2018Fairness of Exposure in Rankingscited by 190
- 2016Unbiased Learning-to-Rank with Biased Feedbackcited by 157
- 2018Equity of Attention: Amortizing Individual Fairness in Rankingscited by 124
- 2019Policy Learning for Fairness in Rankingcited by 74
- 2020Controlling Fairness and Bias in Dynamic Learning-to-Rankcited by 68
- 2022Off-Policy Evaluation for Large Action Spaces via Embeddingscited by 54
- 2020Correcting for Selection Bias in Learning-to-rank Systemscited by 35
- 2022Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Modelcited by 30
- 2021Fairness in Ranking under Uncertaintycited by 27
- 2018Differentiable Unbiased Online Learning to Rankcited by 24
- 2022Doubly-Robust Estimation for Correcting Position-Bias in Click Feedback for Unbiased Learning to Rankcited by 18
15 more
- 2019From Importance Sampling to Doubly Robust Policy Gradientcited by 17
- 2019Policy-Gradient Training of Fair and Unbiased Ranking Functionscited by 16
- 2020Cascade Model-based Propensity Estimation for Counterfactual Learning to Rankcited by 15
- 2020Unifying Online and Counterfactual Learning to Rankcited by 15
- 2023Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk Minimizationcited by 11
- 2021Maximizing Marginal Fairness for Dynamic Learning to Rankcited by 10
- 2022Reaching the End of Unbiasedness: Uncovering Implicit Limitations of Click-Based Learning to Rankcited by 10
- 2020Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Rankingcited by 8
- 2021Understanding and Mitigating the Effect of Outliers in Fair Rankingcited by 6
- 2020Learning from User Interactions with Rankings: A Unification of the Fieldcited by 5
- 2020Counterfactual Learning to Rank using Heterogeneous Treatment Effect Estimationcited by 4
- 2022Probabilistic Permutation Graph Search: Black-Box Optimization for Fairness in Rankingcited by 4
- 2022Unbiased Top-k Learning to Rank with Causal Likelihood Decompositioncited by 2
- 2022Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletioncited by 2
- 2019Unbiased Learning to Rank: Counterfactual and Online Approachescited by 1
Built on by
Citing papers per year: 2026: 1.
1 paper.