用扩散模型生成排序结果，首次将生成式方法引入传统排序任务。
DenoiseRank: Learning to Rank by Diffusion Models
- 通过扩散过程噪声标签，逆过程还原预测分布。
- 在基准数据集上优于传统判别模型，验证生成式排序有效性。
- 为生成式排序提供新范式，适合关注生成建模的学者。
学习排序（LTR）是机器学习的核心任务之一。传统LTR模型虽取得显著进展，但几乎均基于判别视角构建。本文提出一种新型生成式排序模型DenoiseRank，首次从生成视角解决传统LTR问题。该模型在扩散过程中对相关标签进行噪声处理，并在反向过程中基于查询文档逐步去噪，以准确预测标签分布。实验在多个基准数据集上验证了DenoiseRank的有效性，表明其作为生成式LTR任务的基准潜力。
原文摘要 · Abstract (English)
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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