Recent work has proposed stochastic Plackett-Luce (PL) ranking models as a robust choice for optimizing relevance and fairness metrics. Unlike their deterministic counterparts that require heuristic optimization algorithms, PL models are fully differentiable. Theoretically, they can be used to optimize ranking metrics via stochastic gradient descent. However, in practice, the computation of the gradient is infeasible because it requires one to iterate over all possible permutations of items. Consequently, actual applications rely on approximating the gradient via sampling techniques. In this paper, we introduce a novel algorithm: PL-Rank, that estimates the gradient of a PL ranking model w.r.t. both relevance and fairness metrics. Unlike existing approaches that are based on policy gradients, PL-Rank makes use of the specific structure of PL models and ranking metrics. Our experimental analysis shows that PL-Rank has a greater sample-efficiency and is computationally less costly than existing policy gradients, resulting in faster convergence at higher performance. PL-Rank further enables the industry to apply PL models for more relevant and fairer real-world ranking systems.

中文翻译：

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针对相关性和公平性的Plackett-Luce排序模型的计算有效优化

最近的工作提出了随机Plackett-Luce（PL）排名模型，作为优化相关性和公平性指标的可靠选择。与需要启发式优化算法的确定性模型不同，PL模型是完全可区分的。从理论上讲，它们可以用于通过随机梯度下降优化排名指标。但是，实际上，梯度的计算是不可行的，因为它需要一个迭代项的所有可能排列。因此，实际应用依赖于通过采样技术近似梯度。在本文中，我们介绍了一种新颖的算法：PL-Rank，该算法通过相关性和公平性指标来估计PL排名模型的梯度。与基于策略梯度的现有方法不同，PL-Rank利用PL模型的特定结构和排名指标。我们的实验分析表明，PL-Rank与现有的策略梯度相比，具有更高的采样效率，并且在计算上的成本更低，从而以更高的性能实现了更快的收敛。PL-Rank进一步使业界能够将PL模型应用于更相关和更公平的现实世界排名系统。