arXiv Machine Learning By Md Aminul Islam, Kathryn Vasilaky, Elena Zheleva

A Control Function Framework for Mitigating Position Bias in Learning to Rank Systems

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arXiv:2506. 06989v3 Announce Type: replace-cross Abstract: Learning-to-rank (LTR) systems commonly depend on implicit feedback, such as user clicks, because it is easy to collect and can serve as a valuable signal of user preferences.

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arXiv Machine Learning
Sep 1

Adaptive Doubly Robust Off-Policy Evaluation for Ranking Policies under Diverse User Behavior

The paper introduces Adaptive Doubly Robust (ADR), an off‑policy evaluation method for ranking policies that blends adaptive importance weighting with reward regression to reduce variance. ADR is unbiased when the true user behavior model is known and, under a sufficient condition, achieves lower variance than the prior Adaptive Inverse Propensity Scoring (AIPS) approach. Experiments on synthetic data show that ADR consistently improves mean squared error over AIPS and other ranking OPE estimators across various data sizes and ranking lengths.

By Kosuke Iguchi, Ren Kishimoto