论文标题
哪些技巧对于学习排名很重要？
Which Tricks Are Important for Learning to Rank?
论文作者
论文摘要
如今，最先进的学习到级方法基于提高梯度的决策树（GBDT）。最著名的算法是Lambdamart，它是十多年前提出的。最近，提出了其他几种基于GBDT的排名算法。在本文中，我们在统一设置中彻底分析了这些方法。特别是，我们解决以下问题。是否可以直接优化平滑的排名损失，而不是优化凸代替代物？如何正确构建和平滑替代排名损失？为了解决这些问题，我们将Lambdamart与Yerirank和Stochasticrank方法及其修改进行了比较。我们还提出了对Yerirank方法的简单改进，可以优化特定的排名损失功能。结果，我们获得了对学习到级技术的见解，并获得了一种新的最先进的算法。
Nowadays, state-of-the-art learning-to-rank methods are based on gradient-boosted decision trees (GBDT). The most well-known algorithm is LambdaMART which was proposed more than a decade ago. Recently, several other GBDT-based ranking algorithms were proposed. In this paper, we thoroughly analyze these methods in a unified setup. In particular, we address the following questions. Is direct optimization of a smoothed ranking loss preferable over optimizing a convex surrogate? How to properly construct and smooth surrogate ranking losses? To address these questions, we compare LambdaMART with YetiRank and StochasticRank methods and their modifications. We also propose a simple improvement of the YetiRank approach that allows for optimizing specific ranking loss functions. As a result, we gain insights into learning-to-rank techniques and obtain a new state-of-the-art algorithm.
