mlsanityData to 14 Oct 2026

Handling Heterophily in Recommender Systems with Wavelet Hypergraph Diffusion

arXiv 2501.14399 ↗First posted 24 Jan 2025cs.IR cs.AI cs.DBcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 32 of its 70 references that are in the map. First posted on arXiv 24 Jan 2025.

Builds on

32 of 70 references.

References · 32

  1. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
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20 more
  1. 2023Representation Learning with Large Language Models for Recommendationcited by 155
  2. 2020Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approachcited by 120
  3. 2021Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendationcited by 101
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  7. 2020Heterogeneous Hypergraph Embedding for Graph Classificationcited by 52
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  12. 2022Self-Supervised Hypergraph Transformer for Recommender Systemscited by 32
  13. 2021Concept-Aware Denoising Graph Neural Network for Micro-Video Recommendationcited by 18
  14. 2022Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Predictioncited by 18
  15. 2024Manipulating Recommender Systems: A Survey of Poisoning Attacks and Countermeasurescited by 14
  16. 2022Detecting Rumours with Latency Guarantees using Massive Streaming Datacited by 12
  17. 2022Model-Agnostic and Diverse Explanations for Streaming Rumour Graphscited by 9
  18. 2024Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew Resiliencecited by 8
  19. 2023Unified Pretraining for Recommendation via Task Hypergraphscited by 4
  20. 2024PDC-FRS: Privacy-preserving Data Contribution for Federated Recommender Systemcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Empowering Contrastive Federated Sequential Recommendation with LLMscited by 0