arXiv:2602.11453v2 Announce Type: replace-cross Abstract: In information retrieval (IR), learning-to-rank (LTR) methods have traditionally limited themselves to discriminative machine learning approaches that model the probability of the document being relevant to the query given some feature representation of the query-document pair. In this work, we propose an alternative denoising diffusion-based deep generative approach to LTR that instead models the full joint distribution over feature vectors and relevance labels. While in the discriminative setting, an over-parameterized ranking model m
The continuous evolution of AI research pushes for alternative models beyond traditional discriminative approaches, leveraging advancements in deep generative models like diffusion.
This new approach to learning-to-rank could significantly improve information retrieval systems by modeling a fuller joint distribution, leading to more accurate and nuanced search results.
The paradigm for developing information retrieval systems may shift from primarily discriminative models to incorporating more robust generative diffusion-based approaches.
- · AI/ML researchers
- · Information retrieval companies
- · E-commerce platforms
- · Companies reliant on outdated LTR models
Improved relevance and accuracy in search engines and recommendation systems.
Reduced need for extensive feature engineering as models learn full distributions autonomously.
Enhanced user experience across digital platforms, potentially impacting content consumption patterns.
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Read at arXiv cs.LG