Handling Heterophily in Recommender Systems with Wavelet Hypergraph Diffusion

TL;DR

This paper introduces FWHDNN, a novel hypergraph neural network framework that effectively models heterophily and multi-scale relationships in recommender systems, improving accuracy and robustness.

Contribution

The paper presents a new wavelet hypergraph diffusion model with multi-level encoding and multi-modal fusion for enhanced recommendation performance.

Findings

Outperforms state-of-the-art methods in accuracy

Demonstrates robustness and scalability

Effectively captures high-order user-item interactions

Abstract

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.