Skip to main content
SpringerLink
Log in

A Graph Convolution Neural Network for User-Group Aided Personalized Session-Based Recommendation

  • Conference paper
  • First Online:
Neural Information Processing (ICONIP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14448))

Included in the following conference series:

  • 533 Accesses

Abstract

Session-based recommendation systems aim to predict the next user interaction based on the items with which the user interacts in the current session. Currently, graph neural network-based models have been widely used and proven more effective than others. However, these session-based models mainly focus on the user-item and item-item relations in historical sessions while ignoring information shared by similar users. To address the above issues, a new graph-based representation, User-item Group Graph, which considers not only user-item and item-item but also user-user relations, is developed to take advantage of natural sequential relations shared by similar users. A new personalized session-based recommendation model is developed based on this representation. It first generates groups according to user-related historical item sequences and then uses a user group preference recognition module to capture and balance between group-item preferences and user-item preferences. Comparison experiments show that the proposed model outperforms other state-of-art models when similar users are effectively grouped. This indicates that grouping similar users can help find deep preferences shared by users from the same group and is instructive in finding the most appropriate next item for the current user.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Chen, T., Wong, R.C.W.: Handling information loss of graph neural networks for session-based recommendation. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1172–1180 (2020). https://doi.org/10.1145/3394486.3403170

  2. Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015). https://doi.org/10.48550/arXiv.1511.06939

  3. Lai, S., Meng, E., Zhang, F., Li, C., Wang, B., Sun, A.: An attribute-driven mirror graph network for session-based recommendation. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1674–1683 (2022). https://doi.org/10.1145/3477495.3531935

  4. Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., Ma, J.: Neural attentive session-based recommendation. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1419–1428 (2017). https://doi.org/10.1145/3132847.3132926

  5. Li, Y., Tarlow, D., Brockschmidt, M., Zemel, R.: Gated graph sequence neural networks. arXiv preprint arXiv:1511.05493 (2015). https://doi.org/10.48550/arXiv.1511.05493

  6. Liu, F., Cheng, Z., Zhu, L., Gao, Z., Nie, L.: Interest-aware message-passing gcn for recommendation. In: Proceedings of the Web Conference 2021, pp. 1296–1305 (2021). https://doi.org/10.1145/3442381.3449986

  7. Liu, Q., Zeng, Y., Mokhosi, R., Zhang, H.: Stamp: short-term attention/memory priority model for session-based recommendation. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1831–1839 (2018). https://doi.org/10.1145/3219819.3219950

  8. Ma, C., Kang, P., Liu, X.: Hierarchical gating networks for sequential recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 825–833 (2019). https://doi.org/10.1145/3292500.3330984

  9. Pan, Z., Cai, F., Chen, W., Chen, H., De Rijke, M.: Star graph neural networks for session-based recommendation. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 1195–1204 (2020). https://doi.org/10.1145/3340531.3412014

  10. Pang, Y., et al.: Heterogeneous global graph neural networks for personalized session-based recommendation. In: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, pp. 775–783 (2022). https://doi.org/10.1145/3488560.3498505

  11. Qiu, R., Li, J., Huang, Z., Yin, H.: Rethinking the item order in session-based recommendation with graph neural networks. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 579–588 (2019). https://doi.org/10.1145/3357384.3358010

  12. Quadrana, M., Karatzoglou, A., Hidasi, B., Cremonesi, P.: Personalizing session-based recommendations with hierarchical recurrent neural networks. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, pp. 130–137 (2017). https://doi.org/10.1145/3109859.3109896

  13. Ren, P., Chen, Z., Li, J., Ren, Z., Ma, J., De Rijke, M.: RepeatNet: a repeat aware neural recommendation machine for session-based recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 4806–4813 (2019). https://doi.org/10.1609/aaai.v33i01.33014806

  14. Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized markov chains for next-basket recommendation. In: Proceedings of the 19th International Conference on World Wide Web, pp. 811–820 (2010). https://doi.org/10.1145/1772690.1772773

  15. Shani, G., Brafman, R.I., Heckerman, D.: An MDP-based recommender system. arXiv e-prints arXiv:1301.0600 (2012)

  16. Tang, J., Wang, K.: Personalized top-n sequential recommendation via convolutional sequence embedding. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 565–573 (2018). https://doi.org/10.1145/3159652.3159656

  17. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)

  18. Wang, S., Cao, L., Wang, Y., Sheng, Q.Z., Orgun, M.A., Lian, D.: A survey on session-based recommender systems. ACM Comput. Surv. (CSUR) 54(7), 1–38 (2021). https://doi.org/10.1145/3465401

    Article  Google Scholar 

  19. Wang, Z., Wei, W., Cong, G., Li, X.L., Mao, X.L., Qiu, M.: Global context enhanced graph neural networks for session-based recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 169–178 (2020). https://doi.org/10.1145/3397271.3401142

  20. Wu, F., Zhang, T., Holanda de Souza A., Jr., Fifty, C., Yu, T., Weinberger, K.Q.: Simplifying graph convolutional networks. arXiv e-prints arXiv:1902.07153 (2019)

  21. Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., Tan, T.: Session-based recommendation with graph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 346–353 (2019). https://doi.org/10.1609/aaai.v33i01.3301346

  22. Xu, C., et al.: Graph contextualized self-attention network for session-based recommendation. In: IJCAI International Joint Conference on Artificial Intelligence, p. 3940 (2019). https://doi.org/10.24963/ijcai.2019/547

  23. Zhang, M., Wu, S., Gao, M., Jiang, X., Xu, K., Wang, L.: Personalized graph neural networks with attention mechanism for session-aware recommendation. IEEE Trans. Knowl. Data Eng. 34(8), 3946–3957 (2020). https://doi.org/10.1109/TKDE.2020.3031329

    Article  Google Scholar 

  24. Zimdars, A., Chickering, D.M., Meek, C.: Using temporal data for making recommendations. arXiv preprint arXiv:1301.2320 (2013)

Download references

Acknowledgements

The work is supported by the National Natural Science Foundation of China (No. 41871286) and the 1331 Engineering Project of Shanxi Province, China.

Author information

Authors and Affiliations

  1. Key Laboratory of Computational Intelligence and Chinese Information Processing Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China

    Hui Wang, Hexiang Bai, Jun Huo & Minhu Yang

Authors
  1. Hui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hexiang Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jun Huo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Minhu Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hexiang Bai .

Editor information

Editors and Affiliations

  1. Central South University, Changsha, China

    Biao Luo

  2. Chinese Academy of Sciences, Beijing, China

    Long Cheng

  3. Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, H., Bai, H., Huo, J., Yang, M. (2024). A Graph Convolution Neural Network for User-Group Aided Personalized Session-Based Recommendation. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14448. Springer, Singapore. https://doi.org/10.1007/978-981-99-8082-6_26

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-8082-6_26

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8081-9

  • Online ISBN: 978-981-99-8082-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation