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08/06/2023
A Lightweight Method for Modeling Confidence in Recommendations with Learned Beta Distributions
Most Recommender Systems (RecSys) do not provide an indication of confid...
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05/04/2023
Recent Advances in the Foundations and Applications of Unbiased Learning to Rank
Since its inception, the field of unbiased learning to rank (ULTR) has r...
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04/26/2023
Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk Minimization
Counterfactual learning to rank (CLTR) relies on exposure-based inverse ...
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09/20/2022
Closing the Gender Wage Gap: Adversarial Fairness in Job Recommendation
The goal of this work is to help mitigate the already existing gender wa...
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06/25/2022
The Bandwagon Effect: Not Just Another Bias
Optimizing recommender systems based on user interaction data is mainly ...
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06/24/2022
Reaching the End of Unbiasedness: Uncovering Implicit Limitations of Click-Based Learning to Rank
Click-based learning to rank (LTR) tackles the mismatch between click fr...
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05/10/2022
State Encoders in Reinforcement Learning for Recommendation: A Reproducibility Study
Methods for reinforcement learning for recommendation (RL4Rec) are incre...
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04/22/2022
Learning-to-Rank at the Speed of Sampling: Plackett-Luce Gradient Estimation With Minimal Computational Complexity
Plackett-Luce gradient estimation enables the optimization of stochastic...
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03/31/2022
Doubly-Robust Estimation for Unbiased Learning-to-Rank from Position-Biased Click Feedback
Clicks on rankings suffer from position bias: generally items on lower r...
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11/24/2021
It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences Are Dynamic
User interactions with recommender systems (RSs) are affected by user se...
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05/03/2021
Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness
Recent work has proposed stochastic Plackett-Luce (PL) ranking models as...
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02/11/2021
Robust Generalization and Safe Query-Specialization in Counterfactual Learning to Rank
Existing work in counterfactual Learning to Rank (LTR) has focussed on o...
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12/09/2020
Learning from User Interactions with Rankings: A Unification of the Field
Ranking systems form the basis for online search engines and recommendat...
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12/08/2020
Unifying Online and Counterfactual Learning to Rank
Optimizing ranking systems based on user interactions is a well-studied ...
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08/24/2020
When Inverse Propensity Scoring does not Work: Affine Corrections for Unbiased Learning to Rank
Besides position bias, which has been well-studied, trust bias is anothe...
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07/24/2020
Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Ranking
Counterfactual evaluation can estimate Click-Through-Rate (CTR) differen...
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05/18/2020
Policy-Aware Unbiased Learning to Rank for Top-k Rankings
Counterfactual Learning to Rank (LTR) methods optimize ranking systems u...
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11/27/2019
Actionable Interpretability through Optimizable Counterfactual Explanations for Tree Ensembles
Counterfactual explanations help users understand why machine learned mo...
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07/16/2019
Unbiased Learning to Rank: Counterfactual and Online Approaches
This tutorial covers and contrasts the two main methodologies in unbiase...
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07/15/2019
To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions
Learning to Rank (LTR) from user interactions is challenging as user fee...
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01/29/2019
Optimizing Ranking Models in an Online Setting
Online Learning to Rank (OLTR) methods optimize ranking models by direct...
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11/16/2018
The Potential of Learned Index Structures for Index Compression
Inverted indexes are vital in providing fast key-word-based search. For ...
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09/22/2018
Differentiable Unbiased Online Learning to Rank
Online Learning to Rank (OLTR) methods optimize rankers based on user in...
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05/07/2018
Ranking for Relevance and Display Preferences in Complex Presentation Layouts
Learning to Rank has traditionally considered settings where given the r...
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02/17/2018
Optimizing Interactive Systems with Data-Driven Objectives
Effective optimization is essential for to provide a satisfactory user ...
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11/26/2017
Sensitive and Scalable Online Evaluation with Theoretical Guarantees
Multileaved comparison methods generalize interleaved comparison methods...
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11/26/2017
Balancing Speed and Quality in Online Learning to Rank for Information Retrieval
In Online Learning to Rank (OLTR) the aim is to find an optimal ranking ...
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09/07/2016
