Interdependent-path Recurrent Embedding for Knowledge Graph-aware Recommendation

Xiao  Sha; Jianwen  Wang; Xiaoran  Xu; Jianchuan  Ding

doi:10.6180/jase.202603_29(3).0004

Interdependent-path Recurrent Embedding for Knowledge Graph-aware Recommendation

Water Resources Engineering

Xiao Sha , Jianwen Wang, Xiaoran Xu, and Jianchuan Ding

Department of Computer Science, Hebei University of Water Resources and Electric Engineering, Cangzhou, 061001, China

Received: March 3, 2025
Accepted: May 20, 2025
Publication Date: June 15, 2025

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202603_29(3).0004

Knowledge graphs (KGs) have demonstrated their effectiveness in providing high-quality recommendations by incorporating rich semantic relationships between entities. However, existing KG-aware recommendation methods face significant challenges in sufficiently exploiting both the structural and semantic information while maintaining computational efficiency. We propose the Interdependent-path Recurrent Embedding (IPRE) framework that addresses these limitations through novel interdependent path construction and attentive encoding. The framework automatically generates interdependent paths connecting user-item pairs, preserving both semantic relationships and topological dependencies with linear time complexity. A dedicated attentive recurrent network then encodes these paths by learning relation-aware representations and adaptively weighting different predecessors’ influence. Comprehensive experiments on three real-world datasets demonstrate IPRE’s superiority, achieving average improvements of 8.79% in Hit ratio and 9.40% in NDCG over state-of-the-art methods. The framework shows particular effectiveness in sparse data scenarios, while maintaining competitive computational efficiency. These results validate IPRE’s capability to effectively transform KG information into accurate recommendations through its innovative path modeling approach.

Keywords: Recommender Systems; Knowledge Graphs; Attention Mechanism; Collaborative Filtering

[1] Z. Sun, Q. Guo, J. Yang, H. Fang, G. Guo, J. Zhang, and R. Burke, (2019) “Research Commentary on Recommendations with Side Information: A Survey and Research Directions" Electronic Commerce Research and Applications 37: 100879. DOI: 10.1016/j.elerap.2019.100879.
[2] X. Wang, D. Wang, C. Xu, X. He, Y. Cao, and T.-S. Chua. “Explainable Reasoning over Knowledge Graphs for Recommendation”. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence. 2019, 5329–5336. DOI: 10.1609/aaai.v33i01.33015329.
[3] Z. Sun, J. Yang, J. Zhang, A. Bozzon, L.-K. Huang, andC.Xu.“Recurrent Knowledge Graph Embedding for Effective Recommendation”. In: Proceedings of the 12th ACM Conference on Recommender Systems. 2018, 297–305. DOI: 10.1145/3240323.3240361.
[4] C. Shi, B. Hu, W. X. Zhao, and S. Y. Philip, (2018) “Heterogeneous Information Network Embedding for Recommendation" IEEE Transactions on Knowledge and Data Engineering 31(2): 357–370. DOI: 10.1109/TKDE.2018.2833443.
[5] H. Wang, M. Zhao, X. Xie, W. Li, and M. Guo. “Knowledge Graph Convolutional Networks for Recommender Systems”. In: Proceedings of the 28th World Wide Web Conference. 2019, 3307–3313. DOI: 10.1145/3308558.3313417.
[6] Y. Chen, Y. Yang, Y. Wang, J. Bai, X. Song, and I. King. “Attentive Knowledge-aware graph convolutional networks with collaborative guidance for personalized recommendation”. In: 2022 IEEE 38th International Conference on Data Engineering (ICDE). IEEE. 2022, 299–311. DOI: 10.1109/ICDE53745.2022.00027.
[7] H. Wang, F. Zhang, M. Zhang, J. Leskovec, M. Zhao, W. Li, and Z. Wang. “Knowledge-aware graph neural networks with label smoothness regularization for recommender systems”. In: Proceedings of the 25th ACMSIGKDD International Conference on Knowledge Discover and Data Mining. 2019, 968–977. DOI: 10.1145/3292500.3330836.
[8] X. Sha, J. Wang, J. Ding, and X. Xu, (2024) “Hierarchical Knowledge graph embedding with self-attention mechanism for recommendation" Journal of Frontiers of Computer Science and Technology: 1–15. DOI: 10.3778/j.issn.1673-9418.2407019.
[9] Z. Liu, V. W. Zheng, Z. Zhao, Z. Li, H. Yang, M. Wu, and J. Ying. “Interactive Paths Embedding for Se mantic Proximity Search on Heterogeneous Graphs”. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2018, 1860–1869. DOI: 10.1145/3219819.3219953.
[10] X. Xin, X. He, Y. Zhang, Y. Zhang, and J. Jose. “Relational Collaborative Filtering: Modeling Multiple Item Relations for Recommendation”. In: Proceedings of the 42nd International ACM SIGIR Conference on Re search and Development in Information Retrieval. 2019, 125–134. DOI: 10.1145/3331184.3331188.
[11] Y. Cao, X. Wang, X. He, Z. Hu, and T.-S. Chua. “Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences”. In: Proceedings of the 28th World Wide Web Conference. 2019, 151–161. DOI: 10.1145/3308558.3313705.
[12] C. Chen, M. Zhang, W. Ma, Y. Liu, and S. Ma. “Jointly Non-sampling Learning for Knowledge Graph Enhanced Recommendation”. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020, 189–198. DOI: 10.1145/3397271.3401040.
[13] P. Wang, Y. Fan, L. Xia, W. X. Zhao, S. Niu, and J. Huang. “KERL: A Knowledge-Guided Reinforcement Learning Model for Sequential Recommendation”. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020, 209–218. DOI: 10.1145/3397271.3401134.
[14] C. Wang, M. Zhang, W. Ma, Y. Liu, and S. Ma. “Make it a chorus: Knowledge-and time-aware item modeling for sequential recommendation”. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020, 109–118. DOI: 10.1145/3397271.3401131.
[15] Z. Lin and L. Qu, (2023) “Collaborative Knowledge aware Recommendation based on Neighborhood Negative Sampling" Information Systems 115: 102207. DOI: 10.1016/j.is.2023.102207.
[16] X. Wang, H. Ji, C. Shi, B. Wang, Y. Ye, P. Cui, and P. S. Yu. “Heterogeneous Graph Attention Network”. In: Proceedings of the 28th International Conference on World Wide Web. 2019, 2022–2032. DOI: 10.1145/3308558.3313562.
[17] Y. Lu, Y. Fang, and C. Shi. “Meta-learning on Heterogeneous Information Networks for Cold-startRecommendation”. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2020, 1563–1573. DOI: 10.1145/3394486. 3403207.
[18] K. Yuan, G. Liu, J. Wu, and H. Xiong,(2023)“Semantic and Structural View Fusion Modeling for Social Recommendation" IEEE Transactions on Knowledge and Data Engineering 35(11): 11872–11884. DOI: 10.1109/TKDE.2022.3230972.
[19] X. Wang, T. Huang, D. Wang, Y. Yuan, Z. Liu, X. He, and T.-S. Chua. “Learning Intents Behind Interactions with Knowledge Graph for Recommendation”. In: Proceedings of the Web Conference 2021. 2021, 878–887. DOI: 10.1145/3442381.3450133.
[20] Y. Li, X. Sun, H. Chen, S. Zhang, Y. Yang, and G. Xu, (2024) “Attention Is Not the Only Choice: Counterfactual Reasoning for Path-based Explainable Recommendation" IEEE Transactions on Knowledge and Data Engineering: DOI: 10.1109/TKDE.2024.3373608.
[21] J. Jin, J. Qin, Y. Fang, K. Du, W. Zhang, Y. Yu, Z. Zhang, and A. J. Smola. “An Efficient Neighborhood based Interaction Model for Recommendation on Heterogeneous Graph”. In: Proceedings of the 26th ACMSIGKDD International Conference on Knowledge Discovery and Data Mining. 2020, 75–84. DOI: 10.1145/ 3394486.3403050.
[22] X. Chen, C. Huang, L. Yao, X. Wang, W. Zhang, et al. “Knowledge-guided Deep Reinforcement Learning for Interactive Recommendation”. In: 2020 International Joint Conference on Neural Networks. IEEE. 2020, 1–8. DOI: 10.1109/IJCNN48605.2020.9207010.
[23] X. Wang, X. He, Y. Cao, M. Liu, and T.-S. Chua. “KGAT: Knowledge Graph Attention Network for Recommendation”. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2019, 950–958. DOI: 10.1145/3292500.3330989.
[24] Y. Yang, C. Huang, L. Xia, and C. Li. “Knowledge Graph Contrastive Learning for Recommendation”. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2022, 1434–1443. DOI: 10.1145/3477495.3532009.
[25] W. Zhang, L. Yang, Z. Song, H. P. Zou, K. Xu, L. Fang, and P. S. Yu. “Do We Really Need Graph Convolution During Training? Light Post-Training Graph ODE for Efficient Recommendation”. In: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. 2024, 3248–3258. DOI: 10.1145/3627673.3679773.
[26] B. Shuai, Z. Zuo, B. Wang, and G. Wang. “Dag recurrent Neural Networks for Scene Labeling”. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016, 3620–3629. DOI: 10.1109/CVPR.2016.394.
[27] X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T.-S. Chua. “Neural Collaborative Filtering”. In: Proceedings of the 26th International Conference on World Wide Web. 2017, 173–182. DOI: 10.1145/3038912.3052569.
[28] Y. Lin, Z. Liu, M. Sun, Y. Liu, and X. Zhu. “Learning Entity and Relation Embeddings for Knowledge Graph Completion”. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2015, 2181–2187. DOI: 10.1609/aaai.v29i1.9491.
[29] J. Chung, C. Gulcehre, K. Cho, and Y. Bengio.“Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling”. In: Neur IPS 2014 Workshop on Deep Learning. 2014. DOI: https://doi.org/10.48550/arXiv.1412.3555.
[30] B. Hu, C. Shi, W. X. Zhao, and P. S. Yu. “Leveraging Meta-path based Context for Top-N Recommendation with a Neural Co-Attention Model”. In: Proceed ings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2018, 1531–1540. DOI: 10.1145/3219819.3219965.
[31] X. Yu, X. Ren, Y. Sun, Q. Gu, B. Sturt, U. Khandelwal, B. Norick, and J. Han. “Personalized Entity Recom mendation: A Heterogeneous Information Network Approach”. In: Proceedings of the 7th ACM Interna tional Conference on Web Search and Data Mining. 2014, 283–292. DOI: 10.1145/2556195.2556259.
[32] S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. “BPR: Bayesian Personalized Rank ing from Implicit Feedback”. In: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. 2009, 452–461. DOI: 10.5555/1795114.1795167.
[33] G. Lee, K. Kim, and K. Shin. “Revisiting Light GCN: Unexpected Inflexibility, Inconsistency, and A Remedy Towards Improved Recommendation”. In: Proceedings of the 18th ACM Conference on Recom mender Systems. 2024, 957–962. DOI: 10.1145/3640457.3688176.
[34] F. Zhang, N. J. Yuan, D. Lian, X. Xie, and W.-Y. Ma. “Collaborative Knowledge Base Embedding for Rec ommender Systems”. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016, 353–362. DOI: 10.1145/ 2939672.2939673.
[35] Y. Jiang, Y. Yang, L. Xia, and C. Huang. “DiffKG: Knowledge Graph Diffusion Model for Recommen dation”. In: Proceedings of the 17th ACM International Conference on Web Search and Data Mining. 2024, 313 321. DOI: 10.1145/3616855.3635850.
[36] D. P. Kingma and J. Ba, (2014) “Adam: A Method for Stochastic Optimization" arXiv preprint: arXiv:1412.6980. DOI: https://arxiv.org/abs/1412. 6980.
[37] X. Wang, X. He, M. Wang, F. Feng, and T.-S. Chua. “Neural Graph Collaborative Filtering”. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2019, 165–174. DOI: 10.1145/3331184.3331267.
[38] L. Zimmer, M. Lindauer, and F. Hutter, (2021) “Auto Pytorch: Multi-fidelity Metalearning for Efficient and Ro bust Autodl" IEEE Transactions on Pattern Analy sis and Machine Intelligence 43(9): 3079–3090. DOI: 10.1109/TPAMI.2021.3067763.
[39] T. Zhao, J. Chen, Y. Ru, Q. Lin, Y. Geng, and J. Liu. “Untargeted Adversarial Attack on Knowledge Graph Embeddings”. In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2024, 1701–1711. DOI: 10.1145/3626772.3657702.
[40] Z. Guo, P. Song, C. Feng, K. Yao, C. Dang, and J. Liang, (2024) “Causal Intervention for Knowledge Graph Denoising in Recommender Systems" International Journal of Machine Learning and Cybernetics: 1–17. DOI: 10.1007/s13042-024-02500-0.