Learning to Rank (LTR) methods are vital in online economies, affecting users
and item providers. Fairness in LTR models is crucial to allocate exposure
proportionally to item relevance. The deterministic ranking model can lead to
unfair exposure distribution when items with the same relevance receive
slightly different scores. Stochastic LTR models, incorporating the
Plackett-Luce (PL) model, address fairness issues but have limitations in
computational cost and performance guarantees. To overcome these limitations,
we propose FairLTR-RC, a novel post-hoc model-agnostic method. FairLTR-RC
leverages a pretrained scoring function to create a stochastic LTR model,
eliminating the need for expensive training. Furthermore, FairLTR-RC provides
finite-sample guarantees on a user-specified utility using distribution-free
risk control framework. By additionally incorporating the Thresholded PL (TPL)
model, we are able to achieve an effective trade-off between utility and
fairness. Experimental results on several benchmark datasets demonstrate that
FairLTR-RC significantly improves fairness in widely-used deterministic LTR
models while guaranteeing a specified level of utility.

本文提出一种基于预训练打分函数的合成模型 FairLTR-RC, 其利用分布无关风险控制框架提供对用户指定效用的有限样本保证，并通过加入阈值 PL 模型实现效用和公平性之间的有效权衡，在多个基准数据集上证明可以显著提高广泛使用的确定性 LTR 模型的公平性同时确保指定水平的效用。