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<p>With the rapid development of the Internet, multimodal information has become increasingly abundant and structurally complex, making multimodal recommendation systems critical in practical applications. In recent years, substantial efforts have been devoted to alleviating data sparsity and cold start issues in recommendation systems to improve their adaptability in complex multimodal recommendation scenarios. However, the existing methods often ignore the uniqueness of noise between different modes when dealing with modal noise, and fail to achieve targeted denoising. During the modality fusion process, the behavioral differences across different modalities are not fully considered, which limits the recommendation performance of the model. Therefore, we propose a novel <b>S</b>pectrum-guided <b>D</b>enoising and Two-Stage <b>F</b>usion for <b>M</b>ultimodal <b>Rec</b>ommendation framework <b>(SDFMRec)</b>. Specifically, we leverage spectral transformation to perform independent denoising for each modality, effectively mitigating intermodal noise interference. Furthermore, we introduce a dual-stage fusion strategy that jointly captures global semantic correlations and local behavioral differences between modalities, improving both the precision and robustness of recommendations. Extensive experiments on four public multimodal recommendation datasets demonstrate that our method significantly outperforms state-of-the-art baselines, validating its effectiveness and superiority.</p>

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Spectrum-guided denoising and two-stage fusion for multimodal recommendation

  • 期刊论文

  • 发表日期：

    2026年3月6日

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  • Fumao Xu,
  • Mingyong Li,
  • Chengying Wu,
  • Yijie Zhu,
  • Hangshen Nong

摘要

With the rapid development of the Internet, multimodal information has become increasingly abundant and structurally complex, making multimodal recommendation systems critical in practical applications. In recent years, substantial efforts have been devoted to alleviating data sparsity and cold start issues in recommendation systems to improve their adaptability in complex multimodal recommendation scenarios. However, the existing methods often ignore the uniqueness of noise between different modes when dealing with modal noise, and fail to achieve targeted denoising. During the modality fusion process, the behavioral differences across different modalities are not fully considered, which limits the recommendation performance of the model. Therefore, we propose a novel Spectrum-guided Denoising and Two-Stage Fusion for Multimodal Recommendation framework (SDFMRec). Specifically, we leverage spectral transformation to perform independent denoising for each modality, effectively mitigating intermodal noise interference. Furthermore, we introduce a dual-stage fusion strategy that jointly captures global semantic correlations and local behavioral differences between modalities, improving both the precision and robustness of recommendations. Extensive experiments on four public multimodal recommendation datasets demonstrate that our method significantly outperforms state-of-the-art baselines, validating its effectiveness and superiority.