Skip to main content

Advertisement

Springer Nature Link
Log in

DEKGCI: A double-ended recommendation model for integrating knowledge graph and user–item interaction graph

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

User–item bipartite graphs and knowledge graphs are frequently employed in recommender systems due to their ability to provide rich information for user and item modeling. However, existing recommender systems predominantly focus on modeling either the user or item individually, with few studies simultaneously considering both aspects. In this paper, we propose a novel double-ended recommendation model, DEKGCI, which aims to fully leverage the advantages of these two information sources. Specifically, DEKGCI harnesses the high-order connectivity information in the user–item bipartite graph to extract user representations, while utilizing the semantic information in the knowledge graph to enrich item representations. By doing so, DEKGCI concurrently learns both user and item representations, effectively capturing the intricate interaction information between users and items. The DEKGCI model was evaluated on three real-world datasets. Computational results demonstrate the high effectiveness of the proposed DEKGCI model compared to seven state-of-the-art reference methods from the literature. In particular, compared to the best-performing KFGAN model, DEKGCI achieved AUC gains of 0.335% in movie recommendations, and F1 gains of 0.023%, 2.203%, and 0.530% in movie, book, and music recommendations, respectively. The code and data are available at https://github.com/miaomiao924/DEKGCI.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

The codes and the data are available at https://github.com/miaomiao924/DEKGCI.

References

  1. Andrea G, Simone N, Fatima G (2022) Algorithmic logics and the construction of cultural taste of the Netflix Recommender System. In: Culture & Society. Media.

  2. Balabanović M, Shoham Y (1997) Fab: content-based, collaborative recommendation. Commun ACM 40(3):66–72

    Article  Google Scholar 

  3. Boeker M, Urman A (2022) An Empirical Investigation of Personalization Factors on TikTok

  4. Breese JS, Heckerman D, Kadie C (2013) Empirical analysis of predictive algorithms for collaborative filtering. Uncertainty in Artificial Intelligence

  5. Chen J, Zhu T, Gong M, Wang Z (2022) A game-based evolutionary clustering with historical information aggregation for personal recommendation. IEEE Transac Emerging Topics Comput Intell 7(2):552–564

    Article  Google Scholar 

  6. Cheng HT, Koc L, Harmsen J, Shaked T, Chandra T, Aradhye H, Anderson G, Corrado G, Chai W, Ispir M (2016) Wide and deep learning for recommender systems. In: Proceedings of the 1st workshop on deep learning for recommender systems 7 10

  7. Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 3:18. https://doi.org/10.1016/j.swevo.2011.02.002

    Article  Google Scholar 

  8. Gao L, Song L, Liu J, Chen B, Shang X (2022) Topology imbalance and relation inauthenticity aware hierarchical graph attention networks for fake news detection. In: Proceedings of the 29th international conference on computational linguistics 4687 4696

  9. He M, Chen J, Gong M, Shao, Z (2023) HDGCN: Dual-channel graph convolutional network with higher-order information for robust feature learning. IEEE Transactions on Emerging Topics in Computing

  10. He X, Deng K, Wang X (2020) LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. In SIGIR '20: the 43rd international acm sigir conference on research and development in information retrieval, ACM

  11. Hu YT, Xiong F, Lu D-Y, Wang XM, Xiong X, Chen H-S (2020) Movie collaborative filtering with multiplex implicit feedbacks. Neurocomputing 398:485–494

    Article  Google Scholar 

  12. Huang J, Han Z, Xu H, Liu H (2022) Adapted transformer network for news recommendation. Neurocomputing 469:119–129

    Article  Google Scholar 

  13. Huang J, Zhao WX, Dou HJ, Wen JR, Chang EY (2018) Improving Sequential Recommendation with Knowledge-Enhanced Memory Network In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval 505 514 ACM

  14. Jamali M, Ester M (2010) A matrix factorization technique with trust propagation for recommendation in social networks. In Acm Conference on Recommender Systems, ACM

  15. Jiang Y, Yang Y, et al. (2024).DiffKG: Knowledge Graph Diffusion Model for Recommendation. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining 313 321

  16. Khalid A, Lundqvist K, Yates A (2022) A literature review of implemented recommendation techniques used in massive open online courses. Expert Syst Appl 187:115926

    Article  Google Scholar 

  17. Li X, Yang XY, Yu J, Qian YR, Zheng JA (2022) Double-ended recommendation algorithm based on knowledge graph convolutional network. Comput Sci Explor 16(1):176–184 ((in Chinese))

    Google Scholar 

  18. Liao S, Widowati R, Hsieh Y (2021) Investigating online social media users’ behaviors for social commerce recommendations. Technol Soc 66:101655

    Article  Google Scholar 

  19. Linden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. In Internet Computing 76 80 IEEE

  20. Liu ZJ, Tang H, Lin Y (2019) Point-Voxel CNN for Efficient 3D Deep Learning

  21. Lu J, Wu D, Li HP, Li J (2013) User acceptance of software as a service: evidence from customers of China’s leading e-commerce company. J Syst Softw 86(8):2034–2044

    Article  Google Scholar 

  22. Maier C, Simovici D (2022) Bipartite graphs and recommendation systems. J Adv Inform Technol 13:3. https://doi.org/10.12720/jait.13.3.249-258

    Article  Google Scholar 

  23. Mcsherry, F, Mironov, I (2009). Differentially Private Recommender Systems: Building Privacy into the Netflix Prize Contenders. ACM

  24. Mooney R.J, Roy L(2000) Content-based book recommending using learning for text categorization, In Proceedings of the fifth ACM conference on Digital libraries 195 204 ACM

  25. Qin Y, Gao C, Wei S, et al.(2023) Learning from hierarchical structure of knowledge graph for recommendation, ACM Transactions on Information Systems 42 1 24

  26. Song L, Li H, Tan Y, Li Z, Shang X (2024) Enhancing enterprise credit risk assessment with cascaded multi-level graph representation learning. Neural Netw 169:475–484

    Article  Google Scholar 

  27. Tu K, Cui P, Wang DX (2021). Conditional Graph Attention Networks for Distilling and Refining Knowledge Graphs in Recommendation. CIKM.s

  28. Verbert K, Manouselis N, Ochoa X (2012) Context-aware recommender systems for learning: a survey and future challenges. IEEE Trans Learn Technol 5(6):318–335

    Article  Google Scholar 

  29. Wang HW, Zhang FZ, Hou M, Xie X, Guo, M, Liu, Q (2018a). SHINE: signed heterogeneous information network embedding for sentiment link prediction. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining 592 600 ACM

  30. Wang H.W, Zhang FZ, Wang J, Zhao M, Li W, Xie X, Guo M (2018b) Ripplenet: Propagating user preferences on the knowledge graph for recommender systems. In Proceedings of the 27th ACM international conference on information and knowledge management 417 426

  31. Wang HW, Zhao M, Xie X (2019) Knowledge graph convolutional networks for recommender systems. In: The world wide web conference, pp 3307–3313

  32. Wang Q, Mao ZD, Wang B (2017) Knowledge graph embedding: a survey of approaches and applications. IEEE Trans Knowl Data Eng 29(12):2724–2743

    Article  Google Scholar 

  33. Wang X, He X, Cao Y (2019a) Kgat: knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD International conference on knowledge discovery & data mining 950 95

  34. Wang, X., He, X., Wang, M. (2019b). Neural graph collaborative filtering. In Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval 165 174 ACM.

  35. Wang, Z., Lin, G., & Tan, H. (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 ACM.

  36. Wei, Y., Wang, X., Nie, L., He, X., Hong, R., & Chua, T. S. (2019, October). MMGCN: Multi-modal graph convolution network for personalized recommendation of micro-video. In Proceedings of the 27th ACM international conference on multimedia 1437 1445

  37. Wu C, Liu S, Zeng ZY (2022) Knowledge graph-based multi-context-aware recommendation algorithm. Inf Sci 595:179–194

    Article  Google Scholar 

  38. Yu, X., Ren, X., Sun, Y.Z., Gu, Q.Q., Sturt, B., Khandelwal, U., Norick, B., & Han, J.W. (2014). Personalized entity recommendation: A heterogeneous information network approach. In Proceedings of the 7th ACM International Conference on Web Search and Data Mining 283 292 ACM

  39. Zhang, F.Z, Yuan, N.J., & Lian, D.F. (2016). Collaborative Knowledge Base Embedding for Recommender Systems. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 353 362 ACM

  40. Zhang, Z.Y., Hua, B.S., & Rosen, D.W. (2019). Rotation invariant convolutions for 3D point clouds deep learning. In Proc of International Conference on 3D Vision 204 213 IEEE.

  41. Zhao, H., Yao, Q.M., Li, J.D., Song, Y.Q., & Lee, D.L. (2017). Metagraph based recommendation fusion over heterogeneous information networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 635 644

  42. Zhen, Y., Li, W.J., & Yeung, D.Y. (2009). TagiCoFi: Tag informed collaborative filtering. In Acm Conference on Recommender Systems, ACM.

  43. Zhu G-M, Bin CZ, Gu TL (2019) Neural modeling framework of user preferences based on knowledge graph. Pattern Recog Artif Intell 32(7):661–668

    Google Scholar 

Download references

Acknowledgements

This work was financially supported by the Research Funds from the National Natural Science Foundation of China (Grant No. 62077019).

Author information

Authors and Affiliations

  1. Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, 430079, China

    Yajing Yang, Zeyu Zeng, Shiyun Jiang, Mao Chen & Ruirui Shang

Authors
  1. Yajing Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Zeyu Zeng
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Shiyun Jiang
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Mao Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Ruirui Shang
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Y.Y. contributed to the conceptualization and methodology of the manuscript. Z.Z. drafted the main manuscript. S.J. refined the manuscript. M.C. reviewed and edited the manuscript. R.S. conducted the experiments and prepared the figures and tables. All authors reviewed the manuscript."

Corresponding author

Correspondence to Mao Chen.

Ethics declarations

Competing interests

The authors declare no competing interests.

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.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, Y., Zeng, Z., Jiang, S. et al. DEKGCI: A double-ended recommendation model for integrating knowledge graph and user–item interaction graph. J Supercomput 80, 24781–24800 (2024). https://doi.org/10.1007/s11227-024-06344-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-024-06344-x

Keywords