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. Widely used deterministic LTR models can lead to unfair exposure distribution, especially when items with the same relevance receive slightly different ranking scores. Stochastic LTR models, incorporating the Plackett-Luce (PL) ranking model, address fairness issues but suffer from high training cost. In addition, they cannot provide guarantees on the utility or fairness, which can lead to dramatic degraded utility when optimized for fairness. To overcome these limitations, we propose Inference-time Stochastic Ranking with Risk Control (ISRR), a novel method that performs stochastic ranking at inference time with guanranteed utility or fairness given pretrained scoring functions from deterministic or stochastic LTR models. Comprehensive experimental results on three widely adopted datasets demonstrate that our proposed method achieves utility and fairness comparable to existing stochastic ranking methods with much lower computational cost. In addition, results verify that our method provides finite-sample guarantee on utility and fairness. This advancement represents a significant contribution to the field of stochastic ranking and fair LTR with promising real-world applications.

翻译：学习排序（LTR）方法在在线经济中至关重要，影响用户与物品提供者。LTR模型中的公平性对于根据物品相关性按比例分配曝光量至关重要。广泛使用的确定性LTR模型可能导致不公平的曝光分布，尤其是在相关性相同的物品获得略有差异的排序分数时。结合Plackett-Luce（PL）排序模型的随机LTR模型解决了公平性问题，但训练成本高昂。此外，它们无法对效用或公平性提供保证，当为公平性优化时可能导致效用大幅下降。为克服这些限制，我们提出推理时带风险控制的随机排序（ISRR），一种新颖方法，在推理时基于来自确定性或随机LTR模型的预训练评分函数进行随机排序，并保证效用或公平性。在三个广泛采用的数据集上的全面实验结果表明，我们提出的方法能以更低计算成本达到与现有随机排序方法相当的效用和公平性。此外，结果验证了我们的方法在效用和公平性上提供了有限样本保证。这一进展代表了随机排序和公平LTR领域的重大贡献，具有广阔的实际应用前景。