Search papers, labs, and topics across Lattice.
This paper introduces DDMSR, a dual-level denoising framework for multi-modal sequential recommendation that addresses the Dual-Noise Dilemma by purifying both feature-level redundancy and sequence-level stochasticity. The method employs a graph-based feature denoising module using Laplacian smoothing to filter high-frequency semantic noise, alongside a frequency-domain sequence denoising module that utilizes Fast Fourier Transform for adaptive signal modulation. Experimental results across four benchmark datasets reveal that DDMSR significantly outperforms existing state-of-the-art methods, demonstrating its robustness and efficiency in enhancing user preference modeling.
DDMSR effectively tackles the Dual-Noise Dilemma in multi-modal recommendations, leading to superior performance over traditional methods.
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}}.
