Frequency-refined Graph Convolution Network with Cross-modal Wavelet Denoising for Recommendation

Multimodal recommendation systems effectively address data sparsity with visual and textual features but face two primary challenges: (1) Modal noise contamination, where high-frequency noise introduced by feature extractors distorts the semantic representation of items, and (2) Limited multimodal feature fusion capability, where conventional static fusion strategies struggle to capture fine-grained user preferences across modalities. To address these challenges, we propose the Frequency-refined graph convolutional network with cross-modal wavelet denoising for Recommendation (FreRec). Specifically, for the first challenge, FreRec introduces a dual denoising framework that: (i) employs wavelet transforms to decompose multimodal features into low-frequency semantics and high-frequency details, applying adaptive soft-thresholding to remove noise while preserving essential information; and (ii) implements a degree-sensitive edge pruning strategy to eliminate noisy connections in the user-item interaction graph, preventing noise propagation during message passing. For the second challenge, FreRec proposes an adaptive multimodal fusion approach that: (i) constructs a dual-view graph convolutional framework that jointly models user-item interactions and intra-modal semantic similarities; and (ii) leverages the behavior-guided frequency fusion module to dynamically adjust modality importance based on user behavior patterns, achieving robust fusion that adapts to individual preferences. Extensive experiments on three benchmark datasets validate the superiority of our model.