Handling Heterophily in Recommender Systems with Wavelet Hypergraph Diffusion
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918152708816896 |
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| author | Sakong, Darnbi Nguyen, Thanh Tam |
| author_facet | Sakong, Darnbi Nguyen, Thanh Tam |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_14399 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Handling Heterophily in Recommender Systems with Wavelet Hypergraph Diffusion Sakong, Darnbi Nguyen, Thanh Tam Information Retrieval Artificial Intelligence Databases Machine Learning Social and Information Networks 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. |
| title | Handling Heterophily in Recommender Systems with Wavelet Hypergraph Diffusion |
| topic | Information Retrieval Artificial Intelligence Databases Machine Learning Social and Information Networks |
| url | https://arxiv.org/abs/2501.14399 |