Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance
Abstract
We reevaluate the pairwise learning to rank approach based on neural nets, called RankNet, and present a theoretical analysis of its architecture. We show mathematically that the model can, under certain conditions, learn reflexive, antisymmetric, and transitive relations, enabling simplified training and improved performance. Experimental results on the LETOR MSLR-WEB10K, MQ2007 and MQ2008 datasets show that the model outperforms numerous state-of-the-art methods (including a listwise approach), while being inherently simpler in structure and using a pairwise approach only.
Cite
Text
Köppel et al. "Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance." Machine Learning, 2025. doi:10.1007/S10994-024-06644-6Markdown
[Köppel et al. "Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance." Machine Learning, 2025.](https://mlanthology.org/mlj/2025/koppel2025mlj-pairwise/) doi:10.1007/S10994-024-06644-6BibTeX
@article{koppel2025mlj-pairwise,
title = {{Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance}},
author = {Köppel, Marius and Segner, Alexander and Wagener, Martin and Pensel, Lukas and Karwath, Andreas and Kramer, Stefan},
journal = {Machine Learning},
year = {2025},
pages = {112},
doi = {10.1007/S10994-024-06644-6},
volume = {114},
url = {https://mlanthology.org/mlj/2025/koppel2025mlj-pairwise/}
}