Structured Spectral Reasoning for Frequency-Adaptive Multimodal Recommendation
Wei Yang, Rui Zhong, Yiqun Chen, Chi Lu, Peng Jiang
Multimodal recommendation aims to integrate collaborative signals with heterogeneous content (e.g., images and text), 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 disentangle stable versus 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 unified framework for structured, frequency-aware representation learning in 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 signal reliability through spectral causal masking, suppressing spurious 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 for semantic and structural consistency. Experiments on three real-world benchmarks demonstrate that our method consistently outperforms strong baselines, especially under sparse and cold-start settings. Further analysis confirms that structured spectral modeling improves the robustness, interpretability, and generalizability of multimodal recommendation.