Abstract
As a popular graph learning technique, graph neural networks (GNN) show great advantages in the field of personalized recommendation. Existing GNN-based recommendation methods organized user-item interactions (e.g., click, purchase, review, etc.) as the bipartite graph and captured the higher-order collaborative signal with the aid of the GNN to achieve personalized recommendation. However, there exists two limitations in existing studies. First, core features activating user-item interactions were not be identified, which causes that user-item interactions fail to be accurately exploited at the feature level. Second, existing GNNs ignored the mutual association among neighbors in information propagation, which results in structural signal in the bipartite graph not being sufficiently captured. Towards this end, we developed the core features activated graph dual-attention network, namely C-GDN, for personalized recommendation. Specifically, C-GDN firstly identifies core user and item features activating user-item interactions and employs these core features to initialize the bipartite graph, which effectively optimizes the utilizing of user-item interactions at the feature level. Furthermore, C-GDN designs a novel graph dual-attention network to conduct information propagation, which captures more sufficient structural signal in the bipartite graph by considering information from neighbors as well as their mutual association. Extensive experiments on three benchmark datasets shows that C-GDN outperforms state-of-the-art baselines.
Similar content being viewed by others
Availability of supporting data
Data will be made available on request.
References
Berg, R. v. d., Kipf, T. N., & Welling, M. (2017). Graph convolutional matrix completion. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1–9. https://doi.org/10.48550/arXiv.1706.02263
Deng, X., Liao, G., & Zeng, Y. (2022). Group event recommendation based on a heterogeneous attribute graph considering long-and short-term preferences. Journal of Intelligent Information Systems, pp. 1–27. Springer. https://doi.org/10.1007/s10844-022-00758-w
Forestiero, A. (2022). Heuristic recommendation technique in internet of things featuring swarm intelligence approach. Expert Systems with Applications, vol. 187, p. 115904. Elsevier. https://doi.org/10.1016/j.eswa.2021.115904
Gan, M., & Kwon, O. C. (2022). A knowledge-enhanced contextual bandit approach for personalized recommendation in dynamic domains. Knowledge-Based Systems, vol. 251, p. 109158. Elsevier. https://doi.org/10.1016/j.knosys.2022.109158
Gan, M., & Ma, Y. (2022). Deepinteract: Multi-view features interactive learning for sequential recommendation. Expert Systems with Applications, vol. 204, p. 117305. Elsevier. https://doi.org/10.1016/j.eswa.2022.117305
Gan, M., & Zhang, H. (2023). Viga: A variational graph autoencoder model to infer user interest representations for recommendation. Information Sciences, vol. 640, p. 119039. Elsevier. https://doi.org/10.1016/j.ins.2023.119039
Harper, F. M., & Konstan, J. A. (2015). The movielens datasets: History and context. Acm Transactions on Interactive Intelligent Systems, vol. 5, pp. 1–19. Acm New York, NY, USA. https://doi.org/10.1145/2827872
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. https://doi.org/10.1145/3397271.3401063
He, X., Liao, L., Zhang, H., & et al. (2017). Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182. https://doi.org/10.1145/3038912.3052569
He, Y., Mao, Y., Xie, X., & et al. (2022). An improved recommendation based on graph convolutional network. Journal of Intelligent Information Systems, vol. 59, pp. 801–823. Springer. https://doi.org/10.1007/s10844-022-00727-3
Hu, L., Li, C., Shi, C., & et al. (2020). Graph neural news recommendation with long-term and short-term interest modeling. Information Processing & Management, vol. 57, p. 102142. Elsevier. https://doi.org/10.1016/j.ipm.2019.102142
Kang, W. C., Cheng, D. Z., Yao, T., & et al. (2021). Learning to embed categorical features without embedding tables for recommendation. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 840–850. https://doi.org/10.1145/3447548.3467304
Li, Z., Cui, Z., Wu, S., & et al. (2019). Fi-gnn: Modeling feature interactions via graph neural networks for ctr prediction. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 539–548. https://doi.org/10.1145/3357384.3357951
Liu, H., Zheng, C., Li, D., & et al. (2022). Multi-perspective social recommendation method with graph representation learning. Neurocomputing, vol. 468, pp. 469–481. Elsevier. https://doi.org/10.1016/j.neucom.2021.10.050
Song, Y., Ye, H., Li, M., & et al. (2022). Deep multi-graph neural networks with attention fusion for recommendation. Expert Systems with Applications, vol. 191, p. 116240. Elsevier. https://doi.org/10.1016/j.eswa.2021.116240
Su, Y., Zhang, R. M., Erfani, S., & et al. (2021). Neural graph matching based collaborative filtering. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 849–858. https://doi.org/10.1145/3404835.3462833
Tao, Z., Wei, Y., Wang, X., & et al. (2020) Mgat: Multimodal graph attention network for recommendation. Information Processing & Management, vol. 57, p. 102277. Elsevier. https://doi.org/10.1016/j.ipm.2020.102277
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. https://doi.org/10.1145/3331184.3331267
Wu, L., He, X., Wang, X., & et al. (2022a). A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation. IEEE Transactions on Knowledge and Data Engineering, vol. 35, pp. 4425–4445. IEEE. https://doi.org/10.1109/TKDE.2022.3145690
Wu, X., He, H., Yang, H., & et al. (2023). Pda-gnn: propagation-depth-aware graph neural networks for recommendation. World Wide Web, pp.1–22. Springer. https://doi.org/10.1007/s11280-023-01200-z
Wu, S., Sun, F., Zhang, W., & et al. (2022b). Graph neural networks in recommender systems: a survey. ACM Computing Surveys, vol. 55, pp. 1–37. ACM New York, NY. https://doi.org/10.1145/3535101
Zhang, C., Xue, S., Li, J., & et al. (2023). Multi-aspect enhanced graph neural networks for recommendation. Neural Networks, vol. 157, pp. 90–102. Elsevier. https://doi.org/10.1016/j.neunet.2022.10.001
Zhang, T., Zhao, P., Liu, Y., & et al. (2019). Feature-level deeper self-attention network for sequential recommendation. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp. 4320–4326. https://doi.org/10.5555/3367471.3367642
Zhou, G., Zhu, X., Song, C., & et al. (2018). Deep interest network for click-through rate prediction. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data mining, pp. 1059–1068. https://doi.org/10.1145/3219819.3219823
Acknowledgements
This work was supported by the National Natural Science Foundation of China (nos. 72271024, 71871019).
Funding
This work was supported by the National Natural Science Foundation of China (Nos. 72271024, 71871019).
Author information
Authors and Affiliations
Contributions
Xiongtao Zhang: Conceptualization, Methodology, Data Curation, Software, Validation, Writing - Original Draft, Writing - review & editing. Mingxin Gan: Conceptualization, Writing - review & editing, Supervision, Funding acquisition.
Corresponding author
Ethics declarations
Ethics Approval
Not Applicable.
Competing interests
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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 (e.g. a society or other partner) 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.
About this article
Cite this article
Zhang, X., Gan, M. C-GDN: core features activated graph dual-attention network for personalized recommendation. J Intell Inf Syst (2023). https://doi.org/10.1007/s10844-023-00816-x
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10844-023-00816-x