TSPRank: Bridging Pairwise and Listwise Methods with a Bilinear Travelling Salesman Model (2411.12064v3)

Abstract: 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.

• The paper introduces TSPRank, a novel approach that models ranking as a Travelling Salesman Problem to bridge pairwise and listwise methods.
• It leverages combinatorial optimization to convert complex listwise tasks into simpler pairwise comparisons for enhanced training efficiency.
• Empirical evaluations demonstrate that TSPRank outperforms traditional LETOR techniques across diverse tasks, suggesting broad applicability.

TSPRank: Bridging Pairwise and Listwise Methods with a Bilinear Travelling Salesman Model

This paper proposes TSPRank, a novel approach to the Learning-To-Rank (LETOR) problem, which aims to integrate both pairwise and listwise methods by modeling ranking as a Travelling Salesman Problem (TSP). The TSPRank method is introduced as a solution to overcome the typical limitations faced by traditional LETOR methods. Pairwise models, such as LambdaMART, are adept at optimizing pairwise comparisons but often fail to yield optimal global rankings. Conversely, listwise methods focus on optimizing the entire ranked list but typically involve complex tuning and only marginally outperform robust pairwise models.

Methodology

TSPRank reframes the ranking task as a TSP, a well-understood combinatorial optimization problem that has numerous solution algorithms. It leverages pairwise relationships and employs combinatorial optimization to determine listwise rankings. The approach is designed to be integrated directly with existing backbone models, enhancing their ranking performance.

A key advantage of TSPRank is its ability to transform the complex listwise ranking problem into a series of simpler pairwise comparisons, enabling more effective solutions for LETOR. This paper further introduces two learning methodologies: a local method, which relies on a pre-defined adjacency matrix, and a global end-to-end method which incorporates a TSP solver into its training loop.

Empirical Evaluation

The authors conduct extensive experiments across various backbone models on diverse tasks including stock ranking, information retrieval, and historical events ordering. The results consistently indicate that TSPRank significantly outperforms both pure pairwise and listwise methods. The improvement in performance is attributed to TSPRank's ability to better harness global information during the ranking process.

Implications and Future Directions

TSPRank is positioned as a robust and versatile LETOR model, granting significant improvements in tasks that traditional methods struggle with. Its novel use of a TSP solver for combinatorial optimization in ranking not only addresses existing method limitations but also paves the way for future research on LETOR methodologies that bridge pairwise and listwise techniques.

One of the noteworthy implications of this research is the potential application of TSPRank beyond the datasets explored in this paper, as it can enhance the performance of any embedding-based backbone model. Moreover, the paper hints at the opportunity to further explore and refine the use of alternate TSP solvers or neural approximations to reduce inference latency, especially in larger-scale ranking problems.

In conclusion, TSPRank represents a significant paradigm shift in LETOR research by effectively leveraging the strengths of both pairwise and listwise approaches via advanced combinatorial optimization techniques. Further exploration in integrating this approach with neural networks and real-time applications may yield even more substantial gains in various ranking systems.