用小波超图扩散建模用户-物品异质关系，提升推荐精度与鲁棒性
Handling Heterophily in Recommender Systems with Wavelet Hypergraph Diffusion
- 通过异质感知的超图扩散实现消息传递自适应
- 小波变换捕捉多尺度拓扑结构，提升高阶关联建模能力
- 融合结构与文本信息，适合复杂交互场景的推荐系统
推荐系统在多个领域中对个性化用户体验至关重要。然而，捕捉用户-物品交互中的异质性模式及多维特性仍面临重大挑战。为此，我们提出FWHDNN（基于融合的小波超图扩散神经网络），一种面向超图推荐任务的创新框架。该模型包含三个核心组件：(1) 利用异质性感知超图扩散的交叉差异关系编码器，实现对不同类别标签的消息传递自适应；(2) 基于小波变换的多层级聚类编码器，通过超图神经网络层捕捉多尺度拓扑关系；(3) 集成的多模态融合机制，采用中间与晚期融合策略结合结构与文本信息。在真实数据集上的大量实验表明，FWHDNN在准确率、鲁棒性和可扩展性方面均优于现有最优方法，有效捕获用户与物品间的高阶连接。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。