Fusion-based Wavelet Hypergraph Diffusion Neural Networks (FWHDNN) is an innovative hypergraph-based recommender system framework proposed to capture the heterogeneous patterns and multidimensional characteristics of user-item interactions. This model comprises three main components: (1) a cross-difference relation encoder leveraging heterogeneity-aware hypergraph diffusion; (2) a multi-level cluster-wise encoder using wavelet transform-based hypergraph neural network layers; and (3) an integrated multimodal fusion mechanism that combines structural and textual information. Through extensive experiments on real-world datasets, FWHDNN has been demonstrated to outperform existing state-of-the-art methods in terms of accuracy, robustness, and scalability.
