mlsanityData to 7 Oct 2026

Hgformer: Hyperbolic Graph Transformer for Recommendation

arXiv 2502.15693 ↗First posted 30 Dec 2024cs.IR cs.AI cs.LGcited by 2

Not on the map. Why?

Abstract (arXiv)

The cold start problem is a challenging problem faced by most modern recommender systems. By leveraging knowledge from other domains, cross-domain recommendation can be an effective method to alleviate the cold start problem. However, the modelling distortion for long-tail data, which is widely present in recommender systems, is often overlooked in cross-domain recommendation. In this research, we propose a hyperbolic manifold based cross-domain collaborative filtering model using BiTGCF as the base model. We introduce the hyperbolic manifold and construct new propagation layer and transfer layer to address these challenges. The significant performance improvements across various datasets compared to the baseline models demonstrate the effectiveness of our proposed model.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 29 of its 42 references that are in the map. First posted on arXiv 30 Dec 2024.

Builds on

29 of 42 references.

References · 29

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Inductive Representation Learning on Large Graphscited by 6,284
  3. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,134
  4. 2016Wide & Deep Learning for Recommender Systemscited by 745
  5. 2019Neural Graph Collaborative Filteringcited by 660
  6. 2019Graph Transformer Networkscited by 520
  7. 2019Hyperbolic Graph Convolutional Neural Networkscited by 400
  8. 2018Hyperbolic Neural Networkscited by 393
  9. 2023NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classificationcited by 207
  10. 2023SGFormer: Simplifying and Empowering Transformers for Large-Graph Representationscited by 131
  11. 2021Fully Hyperbolic Neural Networkscited by 107
  12. 2023DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusioncited by 85
17 more
  1. 2019Hyperbolic Graph Attention Networkcited by 69
  2. 2021Lorentzian Graph Convolutional Networkscited by 66
  3. 2021A Hyperbolic-to-Hyperbolic Graph Convolutional Networkcited by 53
  4. 2023Fully Hyperbolic Convolutional Neural Networks for Computer Visioncited by 48
  5. 2018Spectral Collaborative Filteringcited by 34
  6. 2022HRCF: Enhancing Collaborative Filtering via Hyperbolic Geometric Regularizationcited by 34
  7. 2022LTE4G: Long-Tail Experts for Graph Neural Networkscited by 30
  8. 2020EdgeRec: Recommender System on Edge in Mobile Taobaocited by 29
  9. 2023On Generalized Degree Fairness in Graph Neural Networkscited by 28
  10. 2023Graph Transformer for Recommendationcited by 28
  11. 2022HICF: Hyperbolic Informative Collaborative Filteringcited by 24
  12. 2021Linear, or Non-Linear, That is the Question!cited by 19
  13. 2023Hyperbolic Contrastive Learningcited by 15
  14. 2022Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformercited by 13
  15. 2024SIGformer: Sign-aware Graph Transformer for Recommendationcited by 12
  16. 2023Alleviating the Long-Tail Problem in Conversational Recommender Systemscited by 5
  17. 2024Hyperbolic Knowledge Transfer in Cross-Domain Recommendation Systemcited by 1

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Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Computationally-efficient Graph Modeling with Refined Graph Random Featurescited by 2
  2. 2025Plug-and-Play Parameter-Efficient Tuning of Embeddings for Federated Recommendationcited by 0