Which Tricks Are Important for Learning to Rank?
This work addresses the optimization of ranking losses for machine learning practitioners, but it is incremental as it builds on existing methods with modifications.
The paper tackles the problem of identifying effective techniques for learning-to-rank by analyzing and comparing gradient-boosted decision tree methods like LambdaMART, YetiRank, and StochasticRank, and proposes a simple improvement to YetiRank that results in a new state-of-the-art algorithm.
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.