Abstract
Recently, Graph Convolutional Network (GCN) has been widely applied in the field of collaborative filtering (CF) with tremendous success, since its message-passing mechanism can efficiently aggregate neighborhood information between users and items. However, most of the existing GCN-based CF models suffer from low convergence rates during training, mainly because they follow the design of standard GCN using a simple uniform average to aggregate the neighborhood information. We also find that the scale of embedding across different layers oscillates. We argue that these issues can be alleviated by our proposed graph convolution framework, namely Accelerated Light Graph Convolutional Network (ALGCN). ALGCN mainly contains two components: influence-aware graph convolution operation and augmentation-free in-batch contrastive loss on the unit sphere. Empirical evaluations on three large and public datasets demonstrate that the proposed method achieves remarkable training speedups over LightGCN and substantially outperforms the state-of-the-art GCN-based CF models. Our method also shows a great improvement in long-tail recommendation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.E.: A simple framework for contrastive learning of visual representations. In: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13–18 July 2020, Virtual Event. Proceedings of Machine Learning Research, vol. 119, pp. 1597–1607. PMLR (2020)
Chen, Y.H., Huang, L., Wang, C.D., Lai, J.H.: Hybrid-order gated graph neural network for session-based recommendation. IEEE Trans. Ind. Inf. 18(3), 1458–1467 (2022)
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2010, Chia Laguna Resort, Sardinia, Italy, 13–15 May 2010. JMLR Proceedings, vol. 9, pp. 249–256. JMLR.org (2010)
Goldberg, D., Nichols, D.A., Oki, B.M., Terry, D.B.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–70 (1992)
He, X., Chen, T., Kan, M.Y., Chen, X.: TriRank: review-aware explainable recommendation by modeling aspects. In: Proceedings of the 24th ACM International Conference on Information and Knowledge Management, CIKM 2015, Melbourne, VIC, Australia, 19–23 October 2015, pp. 1661–1670. ACM (2015)
He, X., Deng, K., Wang, X., Li, Y., Zhang, Y.D., Wang, M.: 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, SIGIR 2020, Virtual Event, China, 25–30 July 2020, pp. 639–648. ACM (2020)
Huang, T., et al.: MixGCF: an improved training method for graph neural network-based recommender systems. In: KDD 2021: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, Singapore, 14–18 August 2021, pp. 665–674. ACM (2021)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. CoRR abs/1609.02907 (2016)
Koren, Y., Bell, R.M., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Lee, D., Kang, S., Ju, H., Park, C., Yu, H.: Bootstrapping user and item representations for one-class collaborative filtering. In: SIGIR 2021: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, 11–15 July 2021, pp. 1513–1522. ACM (2021)
Lin, Z., Tian, C., Hou, Y., Zhao, W.X.: Improving graph collaborative filtering with neighborhood-enriched contrastive learning. In: WWW 2022: The ACM Web Conference 2022, Virtual Event, Lyon, France, 25–29 April 2022, pp. 2320–2329. ACM (2022)
Liu, Y., et al.: Contrastive predictive coding with transformer for video representation learning. Neurocomputing 482, 154–162 (2022)
Miao, H., Li, A., Yang, B.: Meta-path enhanced lightweight graph neural network for social recommendation. In: Database Systems for Advanced Applications - 27th International Conference, DASFAA 2022, Virtual Event, 11–14 April 2022, Proceedings, Part II. vol. 13246, pp. 134–149. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-00126-0_9
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. CoRR abs/1205.2618 (2012)
Wang, T., Isola, P.: Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13–18 July 2020, Virtual Event. Proceedings of Machine Learning Research, vol. 119, pp. 9929–9939. PMLR (2020)
Wang, X., He, X., Wang, M., Feng, F., Chua, T.S.: Neural graph collaborative filtering. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, Paris, France, 21–25 July 2019, pp. 165–174. ACM (2019)
Wu, F., Zhang, T., de Souza, A.H. Jr., Fifty, C., Yu, T., Weinberger, K.Q.: Simplifying graph convolutional networks. CoRR abs/1902.07153 (2019)
Wu, J., et al.: Self-supervised graph learning for recommendation. In: SIGIR 2021: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, 11–15 July 2021, pp. 726–735. ACM (2021)
Yao, T., et al.: Self-supervised learning for large-scale item recommendations. In: CIKM 2021: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, 1–5 November 2021, pp. 4321–4330. ACM (2021)
Yu, J., Xia, X., Chen, T., Cui, L., Hung, N.Q.V., Yin, H.: XSimGCL: towards extremely simple graph contrastive learning for recommendation. CoRR abs/2209.02544 (2022)
Yu, J., Yin, H., Xia, X., Chen, T., Cui, L., Nguyen, Q.V.H.: Are graph augmentations necessary?: Simple graph contrastive learning for recommendation. In: SIGIR 2022: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, 11–15 July 2022, pp. 1294–1303. ACM (2022)
Zhang, X., Sha, C.: Fully utilizing neighbors for session-based recommendation with graph neural networks. In: Database Systems for Advanced Applications - 27th International Conference, DASFAA 2022, Virtual Event, April 11–14, 2022, Proceedings, Part II, vol. 13246, pp. 36–52 (2022). https://doi.org/10.1007/978-3-031-00126-0_3
Acknowledgments
This work was supported by NSFC (62276277 and U1911401), and Guangdong Basic and Applied Basic Research Foundation (2022B1515120059).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Xu, R., Zhao, H., Li, ZY., Wang, CD. (2023). ALGCN: Accelerated Light Graph Convolution Network for Recommendation. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13944. Springer, Cham. https://doi.org/10.1007/978-3-031-30672-3_15
Download citation
DOI: https://doi.org/10.1007/978-3-031-30672-3_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-30671-6
Online ISBN: 978-3-031-30672-3
eBook Packages: Computer ScienceComputer Science (R0)