DMMD4SR: Diffusion Model-based Multi-level Multimodal Denoising for Sequential Recommendation

Multimodal Sequential Recommendation (MMSR) leverages rich item features but often suffers from noisy representations derived from pre-trained models (PTMs). Existing methods neglect critical challenges: (1) domain shift between PTM training data and recommendation scenarios, (2) interest-agnostic noise within modalities (e.g., irrelevant background details), and (3) interaction uncertainty complicating modality fusion. To address these intertwined challenges, we propose DMMD4SR, a novel Diffusion Model-based Multi-level Multimodal Denoising framework for Sequential Recommendation. Inspired by the denoising power of diffusion models, DMMD4SR employs a progressive, multi-level strategy. It includes layers specifically designed to mitigate domain shift noise and context-aware interest-agnostic noise within modalities. Furthermore, an Uncertainty-Guided Modality Denoising Fusion Layer adaptively integrates the purified representations while accounting for interaction uncertainty. Extensive experiments on benchmark datasets demonstrate that DMMD4SR significantly outperforms state-of-the-art baselines, validating the effectiveness of our multi-level denoising approach. The code is available at https://github.com/luweihai/DMMD4SR.