Same Frequency Begets Shared Interests: Popular-Niche Wavelet Graph Learning for Multimodal Recommendation

Multimodal recommendation systems have achieved success in capturing user interests, yet user interests are inherently diverse. Existing GCN-based methods adopt a uniform approach to model user-item interaction graphs, failing to differentiate between ''popular interests'' and ''niche interests''. This deficiency leads to problems such as the information of popular items overshadowing that of niche items during message passing and multimodal fusion, thereby compromising the accuracy and diversity of recommendations. To address these problems, we propose a novel Popular-Niche Graph Wavelet Learning Framework for Multimodal Recommendation (PNGRec). PNGRec first extracts user behavior signals and item popularity signals from the user-item interaction graph, then decomposes the frequency-domain signals of interests into a Popular User-Item Graph and a Niche Graph via frequency-domain signal decomposition, aiming to capture users' popular and niche interests respectively. Furthermore, we introduce a User Interests Four Quadrants Graph Learning mechanism, which enhances the diversity of users' interests under multimodal perception by finely dividing user behavior representations and item popularity representations. Finally, the model is optimized through a self-supervised multi-task joint optimization loss. Extensive experiments on four real-world industrial datasets demonstrate the effectiveness of our proposed interest-divided modeling approach. The source code is publicly available at https://github.com/orangeheyue/PNGRec.