skip to main content
research-article

Knowledge Graph Enhanced Contextualized Attention-Based Network for Responsible User-Specific Recommendation

Published: 29 July 2024 Publication History

Abstract

With ever-increasing dataset size and data storage capacity, there is a strong need to build systems that can effectively utilize these vast datasets to extract valuable information. Large datasets often exhibit sparsity and pose cold start problems, necessitating the development of responsible recommender systems. Knowledge graphs have utility in responsibly representing information related to recommendation scenarios. However, many studies overlook explicitly encoding contextual information, which is crucial for reducing the bias of multi-layer propagation. Additionally, existing methods stack multiple layers to encode high-order neighbor information while disregarding the relational information between items and entities. This oversight hampers their ability to capture the collaborative signal latent in user-item interactions. This is particularly important in health informatics, where knowledge graphs consist of various entities connected to items through different relations. Ignoring the relational information renders them insufficient for modeling user preferences. This work presents an end-to-end recommendation framework named KGCAN (Knowledge Graph Enhanced Contextualized Attention-Based Network), which explicitly encodes both relational and contextual information of entities to preserve the original entity information. Furthermore, a user-specific attention mechanism is employed to capture personalized recommendations. The proposed model is validated on three benchmark datasets through extensive experiments. The experimental results demonstrate that KGCAN outperforms existing knowledge graph based recommendation models. Additionally, a case study from the healthcare domain is discussed, highlighting the importance of attention mechanisms and high-order connectivity in the responsible recommendation system for health informatics.

References

[1]
C. Yin, J. Wang, and J. H. Park. 2017. An improved recommendation algorithm for big data cloud service based on the trust in sociology. Neurocomputing 256 (2017), 49–55. DOI:
[2]
Y. Koren, R. Bell, and C. Volinsky. 2009. Matrix factorization techniques for recommender systems. IEEE Comput. 42, 8 (2009), 30–37.
[3]
M. Jamali and M. Ester. A matrix factorization technique with trust propagation for recommendation in social networks. In Proceedings of the 4th ACM Conference on Recommender Systems (RecSys’10). 135–142. DOI:
[4]
X. He, L. Nie, X. Hu, and T.-S. Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web (WWW’17). 173–182. DOI:
[5]
Y. Tay, L. Anh Tuan, and S. C. Hui. 2018. Latent relational metric learning via memory-based attention for collaborative ranking. In Proceedings of the 2018 World Wide Web Conference. 729–739.
[6]
H. Wang, F. Zhang, J. Wang, M. Zhao, W. Li, X. Xie, and M. Guo. 2018. RippleNet: Propagating user preferences on the knowledge graph for recommender systems. In Proceedings of the International Conference on Information and Knowledge Management (CIKM’18). 417–426. DOI:
[7]
Y. Cao, X. Wang, X. He, Z. Hu, and T. S. Chua. 2019. Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences. In Proceedings of the 2019 World Wide Web Conference. 151–161. DOI:
[8]
Z. Sun, J. Yang, J. Zhang, A. Bozzon, L.-K. Huang, and C. Xu. 2018. Recurrent knowledge graph embedding for effective recommendation. In Proceedings of the 12th ACM Conference on Recommended Systems (RecSys’18). 297–305. DOI:
[9]
B. Hu, C. Shi, W. X. Zhao, and P. S. Yu. 2018. Leveraging meta-path based context for top-N recommendation with a neural co-attention model. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’18). 1531–1540. DOI:
[10]
F. Zhang, N. J. Yuan, D. Lian, X. Xie, and W. Y. Ma. 2016. Collaborative knowledge base embedding for recommender systems. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’16). 353–362. DOI:
[11]
R. Yin, K. Li, G. Zhang, and J. Lu. 2019. A deeper graph neural network for recommender systems. Knowledge-Based Syst. 185 (2019), 105020.
[12]
A. Micheli. 2009. Neural network for graphs: A contextual constructive approach. IEEE Trans. Neural Netw. 20, 3 (2009), 498–511.
[13]
C. Hsu and C. Te Li. 2021. RetaGNN: Relational temporal attentive graph neural networks for holistic sequential recommendation. In Proceedings of the Web Conference 2021 (WWW’21). 2968–2979. DOI:
[14]
Z. Wang, G. Lin, H. Tan, Q. Chen, and X. Liu. 2020. CKAN: Collaborative knowledge-aware attentive network for recommender systems. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 219–228. DOI:
[15]
X. Wang, X. He, Y. Cao, M. Liu, and T.-S. Chua. 2019. KGAT: Knowledge graph attention network for recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 950–958. DOI:
[16]
J. Zhou, G. Cui, S. Hu, Z. Zhang, C. Yang, Z. Liu, L. Wang, C. Li, and M. Sun. 2020. Graph neural networks: A review of methods and applications. AI Open 1 (2020), 57–81.
[17]
T. N. Kipf and M. Welling. 2017. Semi-supervised classification with graph convolutional networks. In Proceedings of the 5th International Conference on Learning Representations (ICLR’17).
[18]
J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun. 2014. Spectral networks and locally connected networks on graphs. In Proceedings of the 5th International Conference on Learning Representations (ICLR’17).
[19]
M. Defferrard, X. Bresson, and P. Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In Proceedings of the 30th International Conference on Neural Information Processing Systems (NIPS’16). 3844–3852.
[20]
A. Micheli. 2009. Neural network for graphs: A contextual constructive approach. IEEE Trans. Neural Netw. 20, 3 (2009), 498–511. DOI:
[21]
W. L. Hamilton, R. Ying, and J. Leskovec. 2017. Inductive representation learning on large graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems. 1025–1035.
[22]
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio. 2018. Graph attention networks. In Proceedings of the 6th International Conference on Learning Representations (ICLR’18).
[23]
X. He, K. Deng, X. Wang, Y. Li, Y. Zhang, and M. Wang. 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 (SIGIR’20). 639–648. DOI:
[24]
F. Liu, Z. Cheng, L. Zhu, Z. Gao, and L. Nie. 2021. Interest-aware message-passing GCN for recommendation. In Proceedings of the Web Conference 2021 (WWW’21). 1296–1305. DOI:
[25]
H. Wang, F. Zhang, X. Xie, and M. Guo. 2018. DKN: Deep knowledge-aware network for news recommendation. In Proceedings of the Web Conference 2018 (WWW ‘18). 1835–1844. DOI:
[26]
H. Wang, M. Zhao, X. Xie, W. Li, and M. Guo. 2019. Knowledge graph convolutional networks for recommender systems. In Proceedings of the Web Conference 2019 (WWW’19), 3307–3313. DOI:
[27]
H. Wang, F. Zhang, M. Zhang, J. Leskovec, M. Zhao, W. Li, and Z. Wang. 2019. Knowledge-aware graph neural networks with label smoothness regularization for recommender systems. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’19). 968–977. DOI:
[28]
X. Yu, X. Ren, Y. Sun, B. Sturt, U. Khandelwal, Q. Gu, B. Norick, and J. Han. Recommendation in heterogeneous information networks with implicit user feedback. In Proceedings of the 7th ACM Conference on Recommended Systems (RecSys’13). 347–350. DOI:
[29]
Y. Dong, N. V. Chawla, and A. Swami. 2017. Metapath2vec: Scalable representation learning for heterogeneous networks. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 135–144. DOI:
[30]
Y. Liu, S. Yang, Y. Xu, C. Miao, M. Wu, and J. Zhang. 2023. Contextualized graph attention network for recommendation with item knowledge graph. IEEE Trans. Knowl. Data Eng. 35, 1 (2023), 181–195. DOI:
[31]
B. Zamanlooy and M. Mirhassani. 2014. Efficient VLSI implementation of neural networks with hyperbolic tangent activation function. IEEE Trans. Very Large Scale Integr. Syst. 22, 1 (2014), 39–48. DOI:
[32]
R. Memisevic, C. Zach, M. Pollefeys, and G. E. Hinton. 2010. Gated softmax classification. In Advances in Neural Information Processing Systems 23 (NIPS’10). 1603–1611.
[33]
H. Wang, F. Zhang, M. Zhang, J. Leskovec, M. Zhao, W. Li, and Z. Wang. 2019. Knowledge-aware graph neural networks with label smoothness regularization for recommender systems. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’19). 968–977, DOI:
[34]
L. Sang, M. Xu, S. Qian, and X. Wu. 2021. Knowledge graph enhanced neural collaborative recommendation. Expert Syst. Appl. 164 (2021), 113992. DOI:
[35]
D. P. Kingma and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv:1412.6980 (2014).
[36]
X. Glorot and Y. Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the 13th International Conference on Artificial Intelligence and Statistics. 249–256.
[37]
G. Shani and A. Gunawardana. 2011. Evaluating recommendation systems. In Recommender Systems Handbook. Springer, 257–297.
[38]
F. Gräßer, S. Kallumadi, H. Malberg, and S. Zaunseder. 2018. Aspect-based sentiment analysis of drug reviews applying cross-domain and cross-data learning. In Proceedings of the 2018 International Conference on Digital Health. 121–125.
[39]
R. Sun, X. Cao, Y. Zhao, J. Wan, K. Zhou, F. Zhang, Z. Wang, and K. Zheng. 2020. Multi-modal knowledge graphs for recommender systems. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management. 1405–1414.
[40]
X. Sha, Z. Sun, and J. Zhang. 2021. Hierarchical attentive knowledge graph embedding for personalized recommendation. Electron. Commer. Res. Appl. 48 (2021), 101071.
[41]
H. Lin, Y. Liu, W. Wang, Y. Yue, and Z. Lin. 2017. Learning entity and relation embeddings for knowledge resolution. Procedia Comput. Sci. 108 (2017), 345–354.

Cited By

View all
  • (2025)Graph attention-based neural collaborative filtering for item-specific recommendation system using knowledge graphExpert Systems with Applications10.1016/j.eswa.2024.126133266(126133)Online publication date: Mar-2025
  • (2024)Predicting medical drug usage intentions via SGD-based text classification modelInternational Advanced Researches and Engineering Journal10.35860/iarej.1495330Online publication date: 14-Dec-2024
  • (2024)Knowledge-Graph-Based Integrated Line Loss Evaluation Management SystemApplied Sciences10.3390/app1420946214:20(9462)Online publication date: 16-Oct-2024
  • Show More Cited By

Index Terms

  1. Knowledge Graph Enhanced Contextualized Attention-Based Network for Responsible User-Specific Recommendation

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Transactions on Intelligent Systems and Technology
    ACM Transactions on Intelligent Systems and Technology  Volume 15, Issue 4
    August 2024
    563 pages
    EISSN:2157-6912
    DOI:10.1145/3613644
    • Editor:
    • Huan Liu
    Issue’s Table of Contents

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 29 July 2024
    Online AM: 22 January 2024
    Accepted: 14 December 2023
    Revised: 18 November 2023
    Received: 03 July 2023
    Published in TIST Volume 15, Issue 4

    Check for updates

    Author Tags

    1. Responsible recommendation system
    2. graph neural network
    3. knowledge graph
    4. attention mechanism

    Qualifiers

    • Research-article

    Funding Sources

    • GIK Institute

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)666
    • Downloads (Last 6 weeks)43
    Reflects downloads up to 12 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2025)Graph attention-based neural collaborative filtering for item-specific recommendation system using knowledge graphExpert Systems with Applications10.1016/j.eswa.2024.126133266(126133)Online publication date: Mar-2025
    • (2024)Predicting medical drug usage intentions via SGD-based text classification modelInternational Advanced Researches and Engineering Journal10.35860/iarej.1495330Online publication date: 14-Dec-2024
    • (2024)Knowledge-Graph-Based Integrated Line Loss Evaluation Management SystemApplied Sciences10.3390/app1420946214:20(9462)Online publication date: 16-Oct-2024
    • (2024)Graph and Sequential Neural Networks in Session-based Recommendation: A SurveyACM Computing Surveys10.1145/369641357:2(1-37)Online publication date: 18-Sep-2024
    • (2024)Practical Challenges and Methodologies in Next Basket Recommendation (NBR)2024 IEEE International Conference on Electro Information Technology (eIT)10.1109/eIT60633.2024.10609841(716-720)Online publication date: 30-May-2024
    • (2024)Optimal Features Driven Attention Network With Medium-Scale Benchmark for Wheat Diseases RecognitionIEEE Access10.1109/ACCESS.2024.343457512(150739-150753)Online publication date: 2024

    View Options

    Login options

    Full Access

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Full Text

    View this article in Full Text.

    Full Text

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media