Structured Spectral Reasoning for Frequency-Adaptive Multimodal Recommendation

• URL: http://arxiv.org/abs/2512.01372v1
• Date: Mon, 01 Dec 2025 07:39:28 GMT
• Title: Structured Spectral Reasoning for Frequency-Adaptive Multimodal Recommendation
• Authors: Wei Yang, Rui Zhong, Yiqun Chen, Chi Lu, Peng Jiang,
• Abstract summary: Multimodal recommendation aims to integrate collaborative signals with heterogeneous content such as visual and textual information.<n>These issues are often exacerbated by naive fusion or shallow modeling strategies, leading to degraded generalization and poor robustness.<n>We propose a Structured Spectral Reasoning framework for frequency-aware multimodal recommendation.
• Score: 13.886659472425393
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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