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arXiv · 2604.20852

DenoiseRank: Learning to Rank by Diffusion Models

Ying Wang·Preslav Nakov·Shangsong Liang
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Abstract

Learning to rank (LTR) is one of the core tasks in Machine Learning. Traditional LTR models have made great progress, but nearly all of them are implemented from discriminative perspective. In this paper, we aim at addressing LTR from a novel perspective, i.e., by a deep generative model. Specifically, we propose a novel denoise rank model, DenoiseRank, which noises the relevant labels in the diffusion process and denoises them on the query documents in the reverse process to accurately predict their distribution. Our model is the first to address traditional LTR from generative perspective and is a diffusion method for LTR. Our extensive experiments on benchmark datasets demonstrated the effectiveness of DenoiseRank, and we believe it provides a benchmark for generative LTR task.

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Ying Wang, Preslav Nakov, Shangsong Liang. 2026-02-17. DenoiseRank: Learning to Rank by Diffusion Models. https://arxiv.org/abs/2604.20852

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