论文标题
Pirank：可扩展的学习以通过可区分的排序排名
PiRank: Scalable Learning To Rank via Differentiable Sorting
论文作者
论文摘要
机器学习方法进行排名的一个关键挑战是感兴趣的性能指标与可以通过基于梯度的方法进行优化的替代损失函数之间的差距。之所以出现此差距，是因为排名指标通常涉及分类操作，而分类操作不是可区分的W.R.T.模型参数。先前的工作提出了与排名指标或简单平滑版本的替代物，并且通常无法扩展到现实世界中的应用程序。我们提出了Pirank，这是一种新的可替代替代物，用于排名，它们采用了基于Neuralsort的分选操作员的连续，温度控制的放松[1]。我们表明，皮兰克（Pirank）准确地恢复了零温度极限的所需指标，并进一步提出了一个鸿沟和互动扩展，在理论和实践中，它都以有利的尺寸缩放到较大的列表大小。从经验上讲，我们证明了较大列表尺寸在培训过程中的作用，并表明皮里克在公开可用的互联网规模学习到级别基准的方法上有显着改善。
A key challenge with machine learning approaches for ranking is the gap between the performance metrics of interest and the surrogate loss functions that can be optimized with gradient-based methods. This gap arises because ranking metrics typically involve a sorting operation which is not differentiable w.r.t. the model parameters. Prior works have proposed surrogates that are loosely related to ranking metrics or simple smoothed versions thereof, and often fail to scale to real-world applications. We propose PiRank, a new class of differentiable surrogates for ranking, which employ a continuous, temperature-controlled relaxation to the sorting operator based on NeuralSort [1]. We show that PiRank exactly recovers the desired metrics in the limit of zero temperature and further propose a divide and-conquer extension that scales favorably to large list sizes, both in theory and practice. Empirically, we demonstrate the role of larger list sizes during training and show that PiRank significantly improves over comparable approaches on publicly available internet-scale learning-to-rank benchmarks.
