Skip to main content
SpringerLink
Log in
Book cover

International Conference on Database Systems for Advanced Applications

DASFAA 2023: Database Systems for Advanced Applications pp 489–504Cite as

Modeling High-Order Relation to Explore User Intent with Parallel Collaboration Views

  • Conference paper
  • First Online:

  • 1813 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13944))

Abstract

As an emerging paradigm, session-based recommendation (SBR) aims to predict the next item by exploiting user behaviors within a short yet anonymous session. Existing works focus on how to effectively model the information based on graph neural networks, which may be insufficient to capture the high-order relation for short-term interest. To this end, we propose a novel framework, named PacoHGNN, which models high-order relations based on HyperGraph Neural Network with Parallel Collaboration views. Specifically, PacoHGNN learns two embedding views for the SBR task, respectively: (i) item-internal graph view, which is to learn the item embedding by modeling pairwise item connectivities among corresponding items; and (ii) session-external hypergraph view, which targets session embedding by learning beyond pairwise information from high-order relations across all sessions. These two types of graph modeling with data-driven can provide complementary information for each other while exhibiting collaboration to some degree. Additionally, we further propose Hyperedge-to-Node (H2N) to enhance supervised signals against the data sparsity problem for better graph representation. Extensive experiments on multiple real-world datasets demonstrate the superiority of the proposed model over state-of-the-art methods.

This is a preview of subscription content, 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   89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   119.99
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

Learn about institutional subscriptions

Notes

  1. 1.

    https://tianchi.aliyun.com/dataset/dataDetail?dataId=42.

  2. 2.

    http://dbis-nowplaying.uibk.ac.at/#nowplaying.

  3. 3.

    https://www.kaggle.com/retailrocket/ecommerce-dataset.

  4. 4.

    https://competitions.codalab.org/competitions/11161.

  5. 5.

    http://2015.recsyschallenge.com/challege.html.

  6. 6.

    https://github.com/hidasib/GRU4Rec.

  7. 7.

    https://github.com/lijingsdu/sessionRec_NARM.

  8. 8.

    https://github.com/uestcnlp/STAMP.

  9. 9.

    https://github.com/CRIPAC-DIG/SR-GNN.

  10. 10.

    https://github.com/RuihongQiu/FGNN.

  11. 11.

    https://github.com/xiaxin1998/DHCN.

References

  1. Bu, J., et al.: Music recommendation by unified hypergraph: combining social media information and music content. In: Bimbo, A.D., Chang, S., Smeulders, A.W.M. (eds.) Proceedings of the 18th International Conference on Multimedia 2010, Firenze, Italy, 25–29 October 2010, pp. 391–400. ACM (2010)

    Google Scholar 

  2. Dias, R., Fonseca, M.J.: Improving music recommendation in session-based collaborative filtering by using temporal context. In: 25th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2013, Herndon, VA, USA, 4–6 November 2013, pp. 783–788. IEEE Computer Society (2013)

    Google Scholar 

  3. Fang, G., Song, J., Wang, X., Shen, C., Wang, X., Song, M.: Contrastive model inversion for data-free knowledge distillation. CoRR abs/2105.08584 (2021). https://arxiv.org/abs/2105.08584

  4. Feng, L., Cai, Y., Wei, E., Li, J.: Graph neural networks with global noise filtering for session-based recommendation. Neurocomputing 472, 113–123 (2022)

    Article  Google Scholar 

  5. Feng, Y., You, H., Zhang, Z., Ji, R., Gao, Y.: Hypergraph neural networks. In: The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, 27 January–1 February 2019, pp. 3558–3565. AAAI Press (2019)

    Google Scholar 

  6. Gong, S., Zhu, K.Q.: Positive, negative and neutral: Modeling implicit feedback in session-based news recommendation. In: Amigó, E., Castells, P., Gonzalo, J., Carterette, B., Culpepper, J.S., Kazai, G. (eds.) SIGIR 2022: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, 11–15 July 2022, pp. 1185–1195. ACM (2022)

    Google Scholar 

  7. Guo, J., et al.: Learning multi-granularity consecutive user intent unit for session-based recommendation. In: Candan, K.S., Liu, H., Akoglu, L., Dong, X.L., Tang, J. (eds.) WSDM 2022: The Fifteenth ACM International Conference on Web Search and Data Mining, Virtual Event/Tempe, AZ, USA, 21–25 February 2022, pp. 343–352. ACM (2022)

    Google Scholar 

  8. Han, Q., Zhang, C., Chen, R., Lai, R., Song, H., Li, L.: Multi-faceted global item relation learning for session-based recommendation. In: Amigó, E., Castells, P., Gonzalo, J., Carterette, B., Culpepper, J.S., Kazai, G. (eds.) SIGIR 2022: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, 11–15 July 2022, pp. 1705–1715. ACM (2022)

    Google Scholar 

  9. Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. In: Bengio, Y., LeCun, Y. (eds.) 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, 2–4 May 2016, Conference Track Proceedings (2016)

    Google Scholar 

  10. Jiao, Y., Xiong, Y., Zhang, J., Zhang, Y., Zhang, T., Zhu, Y.: Sub-graph contrast for scalable self-supervised graph representation learning. In: Plant, C., Wang, H., Cuzzocrea, A., Zaniolo, C., Wu, X. (eds.) 20th IEEE International Conference on Data Mining, ICDM 2020, Sorrento, Italy, 17–20 November 2020, pp. 222–231. IEEE (2020)

    Google Scholar 

  11. Li, A., Cheng, Z., Gao, F.L.Z., Guan, W., Peng, Y.: Disentangled graph neural networks for session-based recommendation. CoRR abs/ arXiv: 2201.03482 (2022)

  12. Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., Ma, J.: Neural attentive session-based recommendation. In: Lim, E., et al. (eds.) Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM 2017, Singapore, 06–10 November 2017, pp. 1419–1428. ACM (2017)

    Google Scholar 

  13. Li, Y., et al.: Hyperbolic hypergraphs for sequential recommendation. In: Demartini, G., Zuccon, G., Culpepper, J.S., Huang, Z., Tong, H. (eds.) CIKM 2021: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, 1–5 November 2021, pp. 988–997. ACM (2021)

    Google Scholar 

  14. Liu, Q., Zeng, Y., Mokhosi, R., Zhang, H.: STAMP: short-term attention/memory priority model for session-based recommendation. In: Guo, Y., Farooq, F. (eds.) Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, 19–23 August 2018, pp. 1831–1839. ACM (2018)

    Google Scholar 

  15. Qiu, R., Huang, Z., Chen, T., Yin, H.: Exploiting positional information for session-based recommendation. ACM Trans. Inf. Syst. 40(2), 35:1–35:24 (2022)

    Google Scholar 

  16. Qiu, R., Li, J., Huang, Z., Yin, H.: Rethinking the item order in session-based recommendation with graph neural networks. In: Zhu, W., et al. (eds.) Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, Beijing, China, 3–7 November 2019, pp. 579–588. ACM (2019)

    Google Scholar 

  17. Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized markov chains for next-basket recommendation. In: Rappa, M., Jones, P., Freire, J., Chakrabarti, S. (eds.) Proceedings of the 19th International Conference on World Wide Web, WWW 2010, Raleigh, North Carolina, USA, 26–30 April 2010, pp. 811–820. ACM (2010)

    Google Scholar 

  18. Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Shen, V.Y., Saito, N., Lyu, M.R., Zurko, M.E. (eds.) Proceedings of the Tenth International World Wide Web Conference, WWW 2010, Hong Kong, China, 1–5 May 2001, pp. 285–295. ACM (2001)

    Google Scholar 

  19. Tan, Y.K., Xu, X., Liu, Y.: Improved recurrent neural networks for session-based recommendations. In: Karatzoglou, A., et al. (eds.) Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, DLRS@RecSys 2016, Boston, MA, USA, 15 September 2016, pp. 17–22. ACM (2016)

    Google Scholar 

  20. Velickovic, P., Fedus, W., Hamilton, W.L., Liò, P., Bengio, Y., Hjelm, R.D.: Deep graph infomax. In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, 6–9 May 2019. OpenReview.net (2019)

    Google Scholar 

  21. Wang, J., Ding, K., Zhu, Z., Caverlee, J.: Session-based recommendation with hypergraph attention networks. In: Demeniconi, C., Davidson, I. (eds.) Proceedings of the 2021 SIAM International Conference on Data Mining, SDM 2021, Virtual Event, 29 April–1 May 2021, pp. 82–90. SIAM (2021)

    Google Scholar 

  22. Wang, Z., Wei, W., Cong, G., Li, X., Mao, X., Qiu, M.: Global context enhanced graph neural networks for session-based recommendation. In: Huang, J., et al. (eds.) Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020, Virtual Event, China, 25–30 July 2020, pp. 169–178. ACM (2020)

    Google Scholar 

  23. Wu, B., Liang, X., Zheng, X., Wang, J., Zhou, X.: Reinforced sample selection for graph neural networks transfer learning. In: Adjeroh, D.A., et al. (eds.) IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022, Las Vegas, NV, USA, 6–8 December 2022, pp. 1281–1288. IEEE (2022)

    Google Scholar 

  24. Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., Tan, T.: Session-based recommendation with graph neural networks. In: The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, 27 January– 1 February 2019, pp. 346–353. AAAI Press (2019)

    Google Scholar 

  25. Xia, X., Yin, H., Yu, J., Wang, Q., Cui, L., Zhang, X.: Self-supervised hypergraph convolutional networks for session-based recommendation. In: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, 2–9 February 2021, pp. 4503–4511. AAAI Press (2021)

    Google Scholar 

  26. Xu, C., Zhao, P., Liu, Y., Sheng, V.S., Xu, J., Zhuang, F., Fang, J., Zhou, X.: Graph contextualized self-attention network for session-based recommendation. In: Kraus, S. (ed.) Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, 10–16 August 2019, pp. 3940–3946. ijcai.org (2019)

    Google Scholar 

  27. Xu, C., et al.: Graph contextualized self-attention network for session-based recommendation. In: IJCAI, vol. 19, pp. 3940–3946 (2019)

    Google Scholar 

  28. You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., Shen, Y.: Graph contrastive learning with augmentations. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, 6–12 December 2020, virtual (2020)

    Google Scholar 

  29. Yu, J., Yin, H., Li, J., Wang, Q., Hung, N.Q.V., Zhang, X.: Self-supervised multi-channel hypergraph convolutional network for social recommendation. In: Leskovec, J., Grobelnik, M., Najork, M., Tang, J., Zia, L. (eds.) WWW 2021: The Web Conference 2021, Virtual Event/Ljubljana, Slovenia, 19–23 April 2021, pp. 413–424. ACM / IW3C2 (2021)

    Google Scholar 

  30. Yu, Z., Zheng, X., Huang, F., Guo, W., Sun, L., Yu, Z.: A framework based on sparse representation model for time series prediction in smart city. Frontiers Comput. Sci. 15(1), 151305 (2021)

    Article  Google Scholar 

  31. Zangerle, E., Pichl, M., Gassler, W., Specht, G.: #nowplaying music dataset: Extracting listening behavior from twitter. In: Zimmermann, R., Yu, Y. (eds.) Proceedings of the First International Workshop on Internet-Scale Multimedia Management, WISMM 2014, Orlando, Florida, USA, 7 November 2014, pp. 21–26. ACM (2014)

    Google Scholar 

  32. Zheng, X., Liang, X., Wu, B., Guo, Y., Tang, H.: Adaptive attention graph capsule network. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022, Virtual and Singapore, 23–27 May 2022, pp. 3588–3592. IEEE (2022)

    Google Scholar 

  33. Zheng, X., Liang, X., Wu, B., Guo, Y., Zhang, X.: Graph capsule network with a dual adaptive mechanism. In: Amigó, E., Castells, P., Gonzalo, J., Carterette, B., Culpepper, J.S., Kazai, G. (eds.) SIGIR 2022: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, 11–15 July 2022, pp. 1859–1864. ACM (2022)

    Google Scholar 

  34. Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., Wang, L.: Graph contrastive learning with adaptive augmentation. In: Leskovec, J., Grobelnik, M., Najork, M., Tang, J., Zia, L. (eds.) WWW 2021: The Web Conference 2021, Virtual Event / Ljubljana, Slovenia, 19–23 April 2021, pp. 2069–2080. ACM / IW3C2 (2021)

    Google Scholar 

Download references

Acknowledgement

This work was supported by National Natural Science Foundation of China (62072463, 71531012), Research Seed Funds of School of Interdisciplinary Studies of Renmin University of China, National Social Science Foundation of China (18ZDA309), and Opening Project of State Key Laboratory of Digital Publishing Technology of Founder Group. The computer resources were provided by Public Computing Cloud Platform of Renmin University of China. Xun Liang is the corresponding author of this paper.

Author information

Authors and Affiliations

  1. School of Information, Renmin University of China, Beijing, China

    Xiangping Zheng, Xun Liang, Bo Wu, Yuhui Guo & Sensen Zhang

  2. Swinburne University of Technology, Melbourne, Australia

    Jun Wang

  3. Qufu Normal University, ShangDong, China

    Yuefeng Ma

Authors
  1. Xiangping Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xun Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bo Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yuhui Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sensen Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yuefeng Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xun Liang .

Editor information

Editors and Affiliations

  1. Tianjin University, Tianjin, China

    Xin Wang

  2. University of Torino, Turin, Italy

    Maria Luisa Sapino

  3. POSTECH, Pohang, Korea (Republic of)

    Wook-Shin Han

  4. University of California Santa Barbara, Santa Barbara, CA, USA

    Amr El Abbadi

  5. University of Auckland, Auckland, New Zealand

    Gill Dobbie

  6. Tianjin University, Tianjin, China

    Zhiyong Feng

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Yingxiao Shao

  8. The University of Queensland, Brisbane, QLD, Australia

    Hongzhi Yin

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zheng, X. et al. (2023). Modeling High-Order Relation to Explore User Intent with Parallel Collaboration Views. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13944. Springer, Cham. https://doi.org/10.1007/978-3-031-30672-3_33

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-30672-3_33

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-30671-6

  • Online ISBN: 978-3-031-30672-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation