Traditional Learning-To-Rank (LETOR) approaches, including pairwise methods like RankNet and LambdaMART, often fall short by solely focusing on pairwise comparisons, leading to sub-optimal global rankings. Conversely, deep learning based listwise methods, while aiming to optimise entire lists, require complex tuning and yield only marginal improvements over robust pairwise models. To overcome these limitations, we introduce Travelling Salesman Problem Rank (TSPRank), a hybrid pairwise-listwise ranking method. TSPRank reframes the ranking problem as a Travelling Salesman Problem (TSP), a well-known combinatorial optimisation challenge that has been extensively studied for its numerous solution algorithms and applications. This approach enables the modelling of pairwise relationships and leverages combinatorial optimisation to determine the listwise ranking. This approach can be directly integrated as an additional component into embeddings generated by existing backbone models to enhance ranking performance. Our extensive experiments across three backbone models on diverse tasks, including stock ranking, information retrieval, and historical events ordering, demonstrate that TSPRank significantly outperforms both pure pairwise and listwise methods. Our qualitative analysis reveals that TSPRank's main advantage over existing methods is its ability to harness global information better while ranking. TSPRank's robustness and superior performance across different domains highlight its potential as a versatile and effective LETOR solution.

翻译：传统的学习排序方法，包括RankNet和LambdaMART等成对方法，通常仅关注成对比较，导致全局排序结果次优。相反，基于深度学习的列表方法虽然旨在优化整个列表，但需要复杂的调参，且相对于稳健的成对模型仅能带来边际改进。为克服这些局限，我们引入了旅行商问题排序方法，一种混合的成对-列表排序方法。TSPRank将排序问题重新定义为旅行商问题，这是一个因其众多求解算法和应用而被广泛研究的著名组合优化难题。该方法能够对成对关系进行建模，并利用组合优化来确定列表排序。该框架可以直接作为附加组件集成到现有骨干模型生成的嵌入中，以提升排序性能。我们在股票排序、信息检索和历史事件排序等多种任务上，对三种骨干模型进行了广泛实验，结果表明TSPRank显著优于纯成对方法和列表方法。我们的定性分析表明，TSPRank相对于现有方法的主要优势在于其排序时能更好地利用全局信息。TSPRank在不同领域的鲁棒性和卓越性能，突显了其作为一种通用且有效的学习排序解决方案的潜力。