Title: Transforming Chaos into Structure: Enhancing Learning to Rank Through Denoising Diffusion

Abstract:

Conventional learning-to-rank (LTR) techniques in information retrieval (IR) have predominantly relied on discriminative machine learning frameworks. These methods typically estimate the likelihood of a document’s relevance to a query based on specific feature representations of the query-document pair. In contrast, this study introduces a novel deep generative approach rooted in denoising diffusion, which aims to model the complete joint distribution of feature vectors and relevance labels.

We posit that while over-parameterized discriminative models may yield various solutions to fit training data, generative models capable of explaining the entire data distribution are better positioned to accurately estimate relevance. Motivated by this insight, we introduce DiffusionRank, a framework that adapts TabDiff—an established denoising diffusion generative model designed for tabular data—to generate generative counterparts of traditional discriminative pointwise and pairwise LTR objectives.

Our comprehensive empirical assessment across four standard LTR datasets reveals that DiffusionRank models outperform their discriminative equivalents. This research highlights a promising avenue for future inquiry, suggesting that ongoing innovations in deep generative modeling, particularly diffusion techniques, can significantly advance learning-to-rank methodologies within the field of information retrieval.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC