Structured Spectral Reasoning for Frequency-Adaptive Multimodal Recommendation

TL;DR

This paper introduces a novel Structured Spectral Reasoning framework that adaptively decomposes, modulates, and fuses spectral signals in multimodal recommendation systems to improve robustness and performance, especially in sparse and cold-start scenarios.

Contribution

It proposes a four-stage spectral reasoning approach that adaptively handles modality-specific noise and enhances semantic alignment in multimodal recommendation.

Findings

Consistent performance improvements over strong baselines.

Enhanced robustness in sparse and cold-start settings.

Clearer understanding of spectral band contributions.

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.