Handling Heterophily in Recommender Systems with Wavelet Hypergraph Diffusion
This work addresses the problem of handling complex user-item interactions in recommender systems for domains requiring personalized recommendations, representing an incremental improvement with novel method components.
The paper tackles the challenge of capturing heterophily and multi-dimensional interactions in recommender systems by introducing FWHDNN, a framework that improves representation learning through heterophily-aware hypergraph diffusion and wavelet transforms, achieving superior accuracy, robustness, and scalability over state-of-the-art methods in experiments on real-world datasets.
Recommender systems are pivotal in delivering personalised user experiences across various domains. However, capturing the heterophily patterns and the multi-dimensional nature of user-item interactions poses significant challenges. To address this, we introduce FWHDNN (Fusion-based Wavelet Hypergraph Diffusion Neural Networks), an innovative framework aimed at advancing representation learning in hypergraph-based recommendation tasks. The model incorporates three key components: (1) a cross-difference relation encoder leveraging heterophily-aware hypergraph diffusion to adapt message-passing for diverse class labels, (2) a multi-level cluster-wise encoder employing wavelet transform-based hypergraph neural network layers to capture multi-scale topological relationships, and (3) an integrated multi-modal fusion mechanism that combines structural and textual information through intermediate and late-fusion strategies. Extensive experiments on real-world datasets demonstrate that FWHDNN surpasses state-of-the-art methods in accuracy, robustness, and scalability in capturing high-order interconnections between users and items.