Beyond Noisy Signals: Dual-Level Denoising for Multi-modal Sequential Recommendation

2026-07-21

AI Summary

This work addresses the challenges posed by feature-level redundancy and sequence-level stochastic noise in multimodal sequential recommendation, which hinder accurate user preference modeling. To this end, the authors propose DDMSR, a novel framework that systematically tackles both types of noise in a unified manner. At the feature level, graph Laplacian smoothing is employed as a low-pass filter to suppress high-frequency semantic noise. At the sequence level, fast Fourier transform combined with learnable frequency-domain filters adaptively attenuates anomalous interaction signals. Furthermore, a multimodal contrastive alignment objective is introduced to enhance cross-modal consistency. Extensive experiments on four public datasets demonstrate that DDMSR significantly outperforms current state-of-the-art methods, confirming its superior robustness and recommendation performance.

Abstract

Multi-modal Sequential Recommendation (SR) incorporates rich side information (e.g., textual and visual features) to enhance dynamic user preference modeling. However, existing frameworks inevitably suffer from a \textbf{Dual-Noise Dilemma}: (1) \textit{Feature-level redundancy} stemming from the semantic gap between generic pre-trained representations and fine-grained recommendation intent; and (2) \textit{Sequence-level stochasticity} induced by spurious interactions such as accidental clicks. To break this bottleneck, we propose \textbf{DDMSR}, a novel \textbf{D}ual-level \textbf{D}enoising \textbf{M}ulti-modal \textbf{S}equential \textbf{R}ecommendation framework that systematically purifies signals from both feature-topological and sequence-frequency perspectives. Specifically, we first design a graph-based feature denoising module that leverages Laplacian smoothing on item semantic graphs as a structural low-pass filter, effectively suppressing high-frequency semantic noise while preserving salient features. For sequence purification, we introduce a frequency-domain sequence denoising module, utilizing the Fast Fourier Transform and a learnable frequency filter to adaptively modulate the interaction spectrum and attenuate anomalous signals. Furthermore, a multi-modal contrastive alignment objective is incorporated to bridge the heterogeneity gap and enforce cross-modal semantic consistency. Extensive experiments on four public benchmark datasets demonstrate that DDMSR consistently outperforms state-of-the-art baselines, providing a highly robust and efficient solution for multi-modal sequential recommendation. The source code is available at: ~\href{https://github.com/jluo00/DDMSR}{\textcolor{blue}{https://github.com/jluo00/DDMSR}}.

Problem

• Dual-Noise Dilemma
• Feature-level redundancy
• Sequence-level stochasticity
• Multi-modal Sequential Recommendation
• Semantic gap

Innovation

• Dual-level Denoising
• Multi-modal Sequential Recommendation
• Laplacian Smoothing
• Frequency-domain Filtering
• Contrastive Alignment