AI 中文总结
研究多模态序列推荐中的双重噪声困境，提出DDMSR框架，从特征拓扑和序列频率角度净化信号，设计基于图的特征去噪与频域序列去噪模块，纳入多模态对比对齐目标，实验证明该框架性能优于基线。
AI 中文摘要
多模态序列推荐（SR）纳入丰富的辅助信息（如文本和视觉特征）以增强动态用户偏好建模。然而，现有框架不可避免地面临“双重噪声困境”：一是通用预训练表示与细粒度推荐意图之间语义差距导致的“特征级冗余”；二是意外点击等虚假交互引起的“序列级随机性”。为突破这一瓶颈，我们提出了DDMSR，一种新颖的双层去噪多模态序列推荐框架，从特征拓扑和序列频率两个角度系统地净化信号。具体而言，我们首先设计了基于图的特征去噪模块，利用拉普拉斯平滑作为结构低通滤波器抑制高频语义噪声并保留显著特征。对于序列净化，引入频域序列去噪模块，利用快速傅里叶变换和可学习频率滤波器自适应调制交互频谱并衰减异常信号。此外，还纳入多模态对比对齐目标以弥合异质性差距并增强跨模态语义一致性。在四个公共基准数据集上的大量实验表明，DDMSR始终优于现有基线，为多模态序列推荐提供了高度稳健且高效的解决方案。
英文摘要
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 Dual-Noise Dilemma: (1) Feature-level redundancy stemming from the semantic gap between generic pre-trained representations and fine-grained recommendation intent; and (2) Sequence-level stochasticity induced by spurious interactions such as accidental clicks. To break this bottleneck, we propose DDMSR, a novel Dual-level Denoising Multi-modal Sequential Recommendation 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: https://github.com/jluo00/DDMSR.
CommentsAccepted by ACM MM 2026. 12 Pages
