Skip to main content
SpringerLink
Log in

Effective hybrid graph and hypergraph convolution network for collaborative filtering

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In recent years, graph convolution networks and hypergraph convolution networks have become a research hotspot in collaborative filtering (CF) because of their information extraction ability in dealing with the user-item interaction information. In particular, hypergraph can model high-order correlation of users and items to achieve better performance. However, the existing graph-based CF methods for mining interactive information remain incomplete and limit the expressiveness of the model. Moreover, they directly use low-order Chebyshev polynomials to fit the convolution kernel of graph and hypergraph without experimental proof or analysis, lacking interpretability. We propose an effective hybrid graph and hypergraph convolutional network (EHGCN) for CF to obtain a capable and interpretable framework. In EHGCN, the graph and the hypergraph are used to model the correlation among nodes in the interaction graph for multilevel learning. EHGCN also optimizes the information flow framework to match the improved convolution strategy of the graph and hypergraph we proposed. Extensive experiments on four real-world datasets show the considerable improvements of EHGCN over other state-of-the-art methods. Moreover, we analyze the graph and hypergraph convolution kernel in terms of the spectral domain to reveal the core of the graph-based CF, which has a heuristic effect on future work.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

LastFM dataset is available at https://github.com/gusye1234/LightGCN-PyTorch. AMusic, AToy, and ML-1M datasets are available at https://github.com/familyld/DeepCF.

Code availability

The source code of EHGCN will be published in https://github.com/RonghuiGuo/EHGCN after paper acceptance for publication.

Notes

  1. https://github.com/makgyver/rectorch.

  2. https://github.com/huangtinglin/NGCF-PyTorch.

  3. https://github.com/newlei/LR-GCCF.

  4. https://github.com/gusye1234/LightGCN-PyTorch.

  5. https://github.com/Wenhui-Yu/LCFN.

References

  1. Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. IEEE Comput 42(8):30–37

    Article  Google Scholar 

  2. He X, Liao L, Zhang H et al (2017) Neural collaborative filtering. In: WWW ’17 proceedings of the 26th international conference on world wide web, pp 173–182

  3. Bruna J, Zaremba W, Szlam A et al (2014) Spectral networks and locally connected networks on graphs. In: ICLR 2014: international conference on learning representations (ICLR) 2014

  4. Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. In: NIPS’16 proceedings of the 30th international conference on neural information processing systems, pp 3844–3852

  5. Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. In: ICLR (Poster)

  6. Wang X, He X, Wang M et al (2019) Neural graph collaborative filtering. In: Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval, pp 165–174

  7. He X, Deng K, Wang X et al (2020) Lightgcn: simplifying and powering graph convolution network for recommendation. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pp 639–648

  8. Chen L, Wu L, Hong R et al (2020) Revisiting graph based collaborative filtering: a linear residual graph convolutional network approach. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 27–34

  9. Wu F, Souza AH, Zhang T et al (2019) Simplifying graph convolutional networks. In: International conference on machine learning, pp 6861–6871

  10. Feng Y, You H, Zhang Z et al (2019) Hypergraph neural networks. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 3558–3565

  11. Ji S, Feng Y, Ji R et al (2020) Dual channel hypergraph collaborative filtering. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 2020–2029

  12. Taubin G (1999) A signal processing approach to fair surface design. ACM

  13. Cui G, Zhou J, Yang C et al (2020) Adaptive graph encoder for attributed graph embedding. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 976–985

  14. Nt H, Maehara T (2019) Revisiting graph neural networks: all we have is low-pass filters. arXiv preprint arXiv:1905.09550

  15. Bo D, Wang X, Shi C et al (2021) Beyond low-frequency information in graph convolutional networks. In: AAAI. AAAI Press

  16. Zhou D, Huang J, Schölkopf B (2006) Learning with hypergraphs: clustering, classification, and embedding. Adv Neural Inf Process Syst 19:1601–1608

    Google Scholar 

  17. Rendle S, Freudenthaler C, Gantner Z et al (2009) Bpr: Bayesian personalized ranking from implicit feedback. In: UAI ’09 proceedings of the twenty-fifth conference on uncertainty in artificial intelligence, pp 452–461

  18. Kingma DP, Ba JL (2015) Adam: a method for stochastic optimization. In: ICLR 2015: international conference on learning representations 2015

  19. Deng ZH, Huang L, Wang CD et al (2019) Deepcf: a unified framework of representation learning and matching function learning in recommender system. In: Proceedings of the AAAI Conference on artificial intelligence, vol 33, pp 61–68

  20. Liang D, Krishnan RG, Hoffman MD et al (2018) Variational autoencoders for collaborative filtering. In: Proceedings of the 2018 world wide web conference, pp 689–698

  21. Yu W, Qin Z (2020) Graph convolutional network for recommendation with low-pass collaborative filters. In: ICML 2020: 37th international conference on machine learning, pp 10,936–10,945

  22. Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. J Mach Learn Res 9:249–256

    Google Scholar 

  23. Golub GH (1970) Singular value decomposition and least squares solutions. Numer Math 14(5):403–420

    Article  MathSciNet  MATH  Google Scholar 

  24. Luo X, Xia Y, Zhu Q (2013) Applying the learning rate adaptation to the matrix factorization based collaborative filtering. Knowl Based Syst 37:154–164

    Article  Google Scholar 

  25. Liu J, Wu C, Liu W (2013) Bayesian probabilistic matrix factorization with social relations and item contents for recommendation. Decis Support Syst 55(3):838–850

    Article  Google Scholar 

  26. Sedhain S, Menon AK, Sanner S et al (2015) Autorec: autoencoders meet collaborative filtering. In: International conference on world wide web

  27. Wu Y, Dubois C, Zheng AX et al (2016) Collaborative denoising auto-encoders for top-n recommender systems. In: The ninth ACM international conference

  28. He X, Du X, Wang X et al (2018) Outer product-based neural collaborative filtering. In: Twenty-seventh international joint conference on artificial intelligence IJCAI-18

  29. Chen M, Wei Z, Huang Z et al (2020) Simple and deep graph convolutional networks. In: ICML 2020: 37th international conference on machine learning, pp 1725–1735

  30. Li G, Muller M, Thabet A et al (2019) Deepgcns: can gcns go as deep as cnns?. In: 2019 IEEE/CVF international conference on computer vision (ICCV), pp 9267–9276

  31. Rong Y, Huang W, Xu T et al (2020) Dropedge: towards deep graph convolutional networks on node classification. In: ICLR 2020: eighth international conference on learning representations

  32. Zhang X, Liu H, Li Q et al (2019) Attributed graph clustering via adaptive graph convolution. In: Twenty-eighth international joint conference on artificial intelligence IJCAI-19

  33. Li X, Hu Y, Sun Y et al (2020) A deep graph structured clustering network. IEEE Access. https://doi.org/10.1109/ACCESS.2020.3020192

    Article  Google Scholar 

  34. Wang C, Pan S, Long G et al (2017) Mgae: marginalized graph autoencoder for graph clustering. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp 889–898

  35. Wang C, Pan S, Hu R et al (2019) Attributed graph clustering: a deep attentional embedding approach. In: Proceedings of the twenty-eighth international joint conference on artificial intelligence, pp 3670–3676

  36. Tong G, Zhu M (2020) Text classification based on graph convolutional network with attention. In: Journal of physics conference, vol 1693, no 1, p 012038

  37. Yao L, Mao C, Luo Y (2019) Graph convolutional networks for text classification. In: 33rd AAAI conference on artificial intelligence, AAAI 2019, 31st annual conference on innovative applications of artificial intelligence, IAAI 2019 and the 9th AAAI symposium on educational advances in artificial intelligence, EAAI 2019, vol 33, pp 7370–7377

  38. Lu Z, Du P, Nie JY (2020) Vgcn-bert: augmenting bert with graph embedding for text classification. In: European conference on information retrieval, pp 369–382

  39. Zhang Z, Shi Y, Yuan C et al (2020) Object relational graph with teacher-recommended learning for video captioning. In: 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 13278–13288

  40. Mohamed A, Qian K, Elhoseiny M et al (2020) Social-stgcnn: a social spatio-temporal graph convolutional neural network for human trajectory prediction. In: 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 14424–14432

  41. Chen ZM, Wei XS, Wang P et al (2019) Multi-label image recognition with graph convolutional networks. In: 2019 IEEE/CVF Conference on computer vision and pattern recognition (CVPR), pp 5177–5186

  42. Berg Rvd, Kipf TN, Welling M (2017) Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263

  43. Zhang J, Shi X, Zhao S et al (2019) Star-gcn: stacked and reconstructed graph convolutional networks for recommender systems. arXiv preprint arXiv:1905.13129

  44. Ying R, He R, Chen K et al (2018) Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 974–983

Download references

Acknowledgements

This paper was supported by the Shandong Provincial Natural Science Foundation (ZR2020MA064).

Author information

Author notes

  1. Xunkai Li and Ronghui Guo have contributed equally and should be regarded as co-first authors.

Authors and Affiliations

  1. School of Mechanical, Electrical and Information Engineering, Shandong University, No.180, Wenhua West Road, Weihai, 264209, Shandong, China

    Xunkai Li, Ronghui Guo, Youpeng Hu, Meixia Qu & Bin Jiang

  2. Wendeng Big Data Bureau, No.288, Tianfu Road, Weihai, 264499, Shandong, China

    Jianwen Chen

Authors
  1. Xunkai Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ronghui Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jianwen Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Youpeng Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Meixia Qu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Bin Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bin Jiang.

Ethics declarations

Conflict of interest

The authors declare that we have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, X., Guo, R., Chen, J. et al. Effective hybrid graph and hypergraph convolution network for collaborative filtering. Neural Comput & Applic 35, 2633–2646 (2023). https://doi.org/10.1007/s00521-022-07735-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07735-y

Keywords

Search

Navigation