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Research Article

A novel Knowledge Graph recommendation algorithm based on Graph Convolutional Network

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Article: 2327441 | Received 27 Jul 2023, Accepted 01 Mar 2024, Published online: 12 Mar 2024

References

  • Balažević, I., Allen, C., & Hospedales, T. M. (2019a, September 17–19). Hypernetwork knowledge graph embeddings//artificial neural networks and machine learning–ICANN 2019. Workshop and special sessions: 28th international conference on artificial neural networks, Munich, Germany, Proceedings 28. Springer International Publishing (pp. 553–565).
  • Balažević, I., Allen, C., & Hospedales, T. M. (2019b). Tucker: Tensor factorization for Knowledge Graph completion. arXiv preprint arXiv:1901.09590.
  • Balazevic, I., Allen, C., & Hospedales, T. M. (2019c). Multi-relational poincaré graph embeddings. Advances in Neural Information Processing Systems, 32.
  • Cao, X., Shi, Y., Wang, J., Yu, H., Wang, X., & Yan, Z. (2022). Cross-modal knowledge graph contrastive learning for machine learning method recommendation. Proceedings of the 30th ACM International Conference on Multimedia (pp. 3694–3702).
  • Chami, I., Wolf, A., Juan, D. C., Sala, F., Ravi, S., & Ré, C. (2020). Low-dimensional hyperbolic Knowledge Graph embeddings. arXiv preprint arXiv:2005.00545.
  • Chen, J., Li, K., Li, K., Yu, P. S., & Zeng, Z. (2021). Dynamic bicycle dispatching of dockless public bicycle-sharing systems using multi-objective reinforcement learning. ACM Transactions on Cyber-Physical Systems, 5(4), 1–24. https://doi.org/10.1145/3447623
  • Chen, S., Liu, X., Gao, J., Jiao, J., Zhang, R., & Ji, Y. (2020). Hitter: Hierarchical transformers for Knowledge Graph embeddings. arXiv preprint arXiv:2008.12813.
  • Chen, X., & Xiao, N. (2023). Recommendation Algorithm Based on Deep Light graph convolution network in knowledge graph. European conference on information retrieval. Cham: Springer Nature Switzerland (pp. 216–231).
  • Degraeve, V., Vandewiele, G., Ongenae, F., & Van Hoecke, S. (2022). R-GCN: The R could stand for random. arXiv preprint arXiv:2203.02424.
  • Elahi, E., & Halim, Z. (2022). Graph attention-based collaborative filtering for user-specific recommender system using knowledge graph and deep neural networks. Knowledge and Information Systems, 64(9), 2457–2480. https://doi.org/10.1007/s10115-022-01709-1
  • Ghorbani, M., Baghshah, M. S., & Rabiee, H. R. (2019). MGCN: Semi-supervised classification in multi-layer graphs with graph convolutional networks. Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (pp. 208–211).
  • Kang, Y., Pu, B., Kou, Y., Yang, Y., Chen, J., Muhammad, K., Yang, P., Xu, L., & Hijji, M. (2023). A deep graph network with multiple similarity for user clustering in human–computer interaction. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 20(2), 1–20.
  • Koke, C. (2023). Limitless stability for Graph Convolutional Networks. arXiv preprint arXiv:2301.11443.
  • Li, Z., Jin, X., Li, W., Guan, S., Guo, J., Shen, H., Wang, Y., & Cheng, X. (2021). Temporal Knowledge Graph reasoning based on evolutional representation learning. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 408–417).
  • Lin, Y., Du, S., Zhang, Y., Duan, K., Huang, Q., & An P. (2022). A recommendation strategy integrating higher-order feature interactions with knowledge graphs. IEEE Access, 10, 119290–119300. https://doi.org/10.1109/ACCESS.2022.3220322
  • Liu, S., Xu, M., Qin, Y., & Lukač, N. (2022). Knowledge graph alignment network with node-level strong fusion. Applied Sciences, 12(19), 9434. https://doi.org/10.3390/app12199434
  • Liu, T., Shen, H., Chang, L., Li, L., & Li, J. (2023). Iterative heterogeneous graph learning for knowledge graph-based recommendation. Scientific Reports, 13(1), 6987. https://doi.org/10.1038/s41598-023-33984-5
  • Niu, H., He, H., Feng, J., Nie, J., Zhang, Y., & Ren, J. (2022). Knowledge graph completion based on GCN of multi-information fusion and high-dimensional structure analysis weight. Chinese Journal of Electronics, 31(2), 387–396. https://doi.org/10.1049/cje.2021.00.080
  • Simon, B., Joliat, D., Weber, E., & Rayment, A. (2023). MFRRI: Research on multi-feature joint recommendation algorithm based on graph neural network.
  • Sun, Z., Deng, Z. H., Nie, J. Y., & Tang, J. (2019). Rotate: Knowledge Graph embedding by relational rotation in complex space. arXiv preprint arXiv:1902.10197.
  • Vashishth, S., Sanyal, S., Nitin, V., & Talukdar, P. (2019). Composition-based multi-relational graph convolutional networks. arXiv preprint arXiv:1911.03082.
  • Wang, H., Zeng, Y., Chen, J., Zhao, Z., & Chen, H. (2022). A spatiotemporal graph neural network for session-based recommendation[J]. Expert Systems with Applications, 202, 117114. http://doi.org/10.1016/j.eswa.2022.117114
  • Wang, Q., Huang, P., Wang, H., Dai, S., Jiang, W., Liu, J., Lyu, Y., Zhu, Y., & Wu, H. (2019). Coke: Contextualized Knowledge Graph embedding. arXiv preprint arXiv:1911.02168.
  • Wang, R., Dong, B., Li, T., Wu, M., Bu, C., & Wu, X. (2023). User interaction-aware knowledge graphs for recommender systems. International conference on database and expert systems applications. Cham: Springer Nature Switzerland (pp. 18–32).
  • Wang, Z., Wang, Z., Li, X., Yu, Z., Guo, B., Chen, L., & Zhou, X. (2022). Exploring multi-dimension user-item interactions with attentional Knowledge Graph neural networks for recommendation. IEEE Transactions on Big Data, 9(1), 212–226.
  • Yang, Y., Huang, C., Xia, L., & Li, X. (2022). Knowledge graph contrastive learning for recommendation. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1434–1443).
  • Ye, R., Li, X., Fang, Y., Zang, H., & Wang, M. (2019). A Vectorized Relational Graph Convolutional Network for Multi-Relational Network Alignment. IJCAI (pp. 4135–4141).
  • You, Y., Chen, T., Wang, Z., & Shen, W. (2020). L2-gcn: Layer-wise and learned efficient training of graph convolutional networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2127–2135).
  • Yu, S., Zhang, S., Zhang, J., Zhou, J., Sun, J., Li, B., & Xuan, Q. (2022). Subgraph networks based entity alignment for cross-lingual knowledge graph. Big data and social computing: 7th China national conference, BDSC 2022, Hangzhou, People’s Republic of China, August 11-13, 2022, Revised selected papers. Singapore: Springer Nature Singapore (pp. 114–128).
  • Zhao, L., Li, K., Pu, B., Chen, J., Li, S., & Liao, X. (2022). An ultrasound standard plane detection model of fetal head based on multi-task learning and hybrid knowledge graph. Future Generation Computer Systems, 135, 234–243. https://doi.org/10.1016/j.future.2022.04.011
  • Zhu, J., Han, X., Deng, H., Tao, C., Zhao, L., Wang, P., Lin, T., & Li, H. (2022). KST-GCN: A knowledge-driven spatial-temporal graph convolutional network for traffic forecasting. IEEE Transactions on Intelligent Transportation Systems, 23(9), 15055–15065. https://doi.org/10.1109/TITS.2021.3136287