Multimodal recommendation aims to integrate collaborative signals with heterogeneous content such as visual and textual information, but remains challenged by modality-specific noise, semantic inconsistency, and unstable propagation over user-item graphs. These issues are often exacerbated by naive fusion or shallow modeling strategies, leading to degraded generalization and poor robustness. While recent work has explored the frequency domain as a lens to separate stable from noisy signals, most methods rely on static filtering or reweighting, lacking the ability to reason over spectral structure or adapt to modality-specific reliability. To address these challenges, we propose a Structured Spectral Reasoning (SSR) framework for frequency-aware multimodal recommendation. Our method follows a four-stage pipeline: (i) Decompose graph-based multimodal signals into spectral bands via graph-guided transformations to isolate semantic granularity; (ii) Modulate band-level reliability with spectral band masking, a training-time masking with a prediction-consistency objective that suppresses brittle frequency components; (iii) Fuse complementary frequency cues using hyperspectral reasoning with low-rank cross-band interaction; and (iv) Align modality-specific spectral features via contrastive regularization to promote semantic and structural consistency. Experiments on three real-world benchmarks show consistent gains over strong baselines, particularly under sparse and cold-start settings. Additional analyses indicate that structured spectral modeling improves robustness and provides clearer diagnostics of how different bands contribute to performance.

翻译：多模态推荐旨在将协同信号与视觉和文本等异构内容信息相结合，但仍面临模态特定噪声、语义不一致性以及在用户-物品图上传播不稳定等挑战。这些问题常因简单的融合或浅层建模策略而加剧，导致泛化能力下降和鲁棒性不足。尽管近期研究探索了将频域作为分离稳定信号与噪声信号的视角，但大多数方法依赖于静态滤波或重新加权，缺乏对谱结构进行推理或适应模态特定可靠性的能力。为应对这些挑战，我们提出了一种面向频率感知多模态推荐的结构化谱推理（SSR）框架。我们的方法遵循四阶段流程：（i）通过图引导变换将基于图的多模态信号分解为谱带，以隔离语义粒度；（ii）利用谱带掩码调节带级可靠性，这是一种训练时掩码技术，通过预测一致性目标抑制脆弱的频率分量；（iii）使用低秩跨带交互的超谱推理融合互补的频率线索；（iv）通过对比正则化对齐模态特定的谱特征，以促进语义和结构一致性。在三个真实世界基准测试上的实验表明，相较于强基线模型取得了持续的性能提升，尤其在稀疏和冷启动场景下。进一步分析表明，结构化谱建模提高了鲁棒性，并为不同谱带如何影响性能提供了更清晰的诊断依据。