[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Exploiting intra- and inter-session dependencies for session-based recommendations

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

Session-based recommender systems (SBRSs) aim at predicting the next item via learning the dynamic and short-term preferences of users. Most of the existing SBRSs usually make predictions based on the intra-session dependencies embedded in session information only, ignoring more complex inter-session dependencies and other available side information (e.g., item attributes, users), which in turn greatly limits the improvement of the recommendation accuracy. In order to effectively extract both intra- and inter-session dependencies from not only the session information but also the side information, to further improve the accuracy of next-item recommendations, we propose a novel hypergraph learning (HL) framework. The HL framework mainly contains three modules, i.e., a hypergraph construction module, a hypergraph learning module, and a next-item prediction module. The hypergraph construction module constructs a hypergraph to connect the users, items and item attributes together in a unified way. Then, the hypergraph learning module learns the informative latent representation for each item by extracting both intra- and inter-session dependencies embedded in the constructed hypergraph. Also, a latent representation for each user is learned. After that, the learned latent representations are fed into the next-item prediction module for next-item recommendations. We conduct extensive experiments on two real-world datasets. The experimental results show that our HL framework outperforms the state-of-the-art approaches.

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
£29.99 /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 includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. https://tianchi.aliyun.com/dataset/dataDetail?dataId=53

  2. https://www.kaggle.com/frtgnn/dunnhumby-the-complete-journey

References

  1. Agarwal, S., Branson, K., Belongie, S.: Higher order learning with graphs. In: ICML, pp 17–24 (2006)

  2. Eirinaki, M., Vazirgiannis, M., Kapogiannis, D.: Web path recommendations based on page ranking and markov models. In: WIDM, pp 2–9 (2005)

  3. Feng, Y., You, H., Zhang, Z., Ji, R., Gao, Y.: Hypergraph neural networks. In: AAAI, pp 3558–3565 (2019)

  4. Gao, Y., Wang, M., Tao, D., Ji, R., Dai, Q.: 3-d object retrieval and recognition with hypergraph analysis. IEEE Trans. Image Process. 21(9), 4290–4303 (2012)

    Article  MathSciNet  Google Scholar 

  5. Hidasi, B., Karatzoglou, A.: Recurrent neural networks with top-k gains for session-based recommendations. In: CIKM, pp 843–852 (2018)

  6. Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session based recommendations with recurrent neural networks. In: ICLR, pp 1–10 (2016)

  7. Hosseini, S., Yin, H., Zhou, X., Sadiq, S., Kangavari, M.R., Cheung, N.-M.: Leveraging multi-aspect time-related influence in location recommendation. World Wide Web J. 22, 1001–1028 (2019)

    Article  Google Scholar 

  8. Huang, Y., Liu, Q., Metaxas, D.: Video object segmentation by hypergraph cut. In: CVPR, pp 1738–1745 (2009)

  9. Jiang, J., Wei, Y., Feng, Y., Cao, J., Gao, Y.: Dynamic hypergraph neural networks. In: IJCAI, pp 2635–2641 (2019)

  10. Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. IEEE Comput. 42(8), 30–37 (2009)

    Article  Google Scholar 

  11. Li, X., Yin, H., Zhou, K., Zhou, X.: Semi-supervised clustering with deep metric learning and graph embedding. World Wide Web J. 23, 781–798 (2020)

    Article  Google Scholar 

  12. Liang, D., Altosaar, J., Charlin, L., Blei, D.M: Factorization meets the item embedding: regularizing matrix factorization with item co-occurrence. In: RecSys, pp 59–66 (2006)

  13. Liu, Q., Wu, S., Wang, L., Tan, T.: Predicting the next location: a recurrent model with spatial and temporal contexts. In: AAAI, pp 194–200 (2016)

  14. Liu, Q., Zeng, Y., Mokhosi, R., Zhang, H.: Stamp: short-term attention/memory priority model for session-based recommendation. In: KDD, pp 1831–1839 (2019)

  15. Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: bayesian personalized ranking from implicit feedback. In: UAI, pp 452–461 (2009)

  16. Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized markov chains for next-basket recommendation. In: WWW, pp 811–820 (2010)

  17. Ricci, F., Rokach, L., Shapira, B., Kantor, P.B.: Recommender Systems Handbook. Springer, Berlin (2011)

    Book  Google Scholar 

  18. Qiu, R., Li, J., Huang, Z., Yin, H.: Rethinking the item order in session-based recommendation with graph neural networks. In: CIKM, pp 579–588 (2019)

  19. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: WWW, pp 285–295 (2001)

  20. Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., Tan, T.: Session-based recommendation with graph neural networks. In: AAAI, pp 346–353 (2018)

  21. Wu, S., Zhang, M., Jiang, X., Xu, K., Wang, L.: Personalizing graph neural networks with attention mechanism for session-based recommendation. arXiv:1910.08887 (2019)

  22. Vinyals, O., Bengio, S., Kudlur, M.: Order matters: sequence to sequence for sets. In: ICLR (2016)

  23. Wang, S., Cao, L., Wang, Y., Sheng, Q.Z., Orgun, M., Lian, D.: A survey on session-based recommender systems. ACM Computing Surveys (CSUR) 54(7), 1–38 (2021)

    Article  Google Scholar 

  24. Wang, Y., Feng, C., Chen, L., Yin, H., Guo, C., Chu, Y.: User identity linkage across social networks via linked heterogeneous network embedding. World Wide Web J. 22, 2611–2632 (2019)

    Article  Google Scholar 

  25. Wang, S., Hu, L., Cao, L., Huang, X., Lian, D., Liu, W.: Attention-based transactional context embedding for next-item recommendation. In: AAAI, pp 2532–2539 (2018)

  26. Wang, S., Hu, L., Wang, Y., He, X., Sheng, Q.Z., Orgun, M., Cao, L., Francesco, R., Yu, P.S.: Graph learning based recommender systems: a review. In: IJCAI, pp 1–9 (2021)

  27. Wang, S., Hu, L., Wang, Y., Sheng, Q.Z., Orgun, M., Cao, L.: Modeling multi-purpose sessions for next-item recommendations via mixture-channel purpose routing networks. In: IJCAI, pp 3771–3777 (2019)

  28. Wang, S., Hu, L., Wang, Y., Sheng, Q.Z., Orgun, M., Cao, L.: Intention nets: psychology-inspired user choice behavior modeling for next-basket prediction. In: AAAI, pp 6259–6266 (2020)

  29. Wang, N., Wang, S., Wang, Y., Sheng, Q.Z., Orgun, M.: Modelling local and global dependencies for next-item recommendations. In: WISE, pp 285–300 (2020)

  30. 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: SIGIR, pp 169–178 (2020)

  31. Xu, C., Zhao, P., Liu, Y., Seng, V.S., Xu, J., Zhuang, F., Fang, J., Zhou, X.: Graph contextualized self attention network for session-based recommendation. In: IJCAI, pp 3940–3946 (2019)

  32. Yu, F., Zhu, Y., Liu, Q., Wu, S., Wang, L., Tan, T.: TAGNN: target attentive graph neural networks for session-based recommendation. In: SIGIR, pp 1–5 (2020)

  33. Yuan, F., Karatzoglou, A., Arapakis, I., Jose, J.M., He, X.: A simple convolutional generative network for next item recommendation. In: WSDM, pp 582–590 (2019)

  34. Zhou, D., Huang, J., Scholkopf, B.: Learning with hypergraphs: clustering, classification, and embedding. In: NIPS, pp 1601–1608 (2007)

  35. Peng, M., Zeng, G., Sun, Z., Huang, J., Wang, Hua., Tian, Gang.: Personalized app recommendation based on app permissions. World Wide Web J. 21(1), 89–104 (2018)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by ARC (Australian Research Council) Discovery Project DP180102378.

Author information

Authors and Affiliations

  1. Department of Computing, Macquarie University, Sydney, Australia

    Nan Wang, Shoujin Wang, Yan Wang, Quan Z. Sheng & Mehmet A. Orgun

Authors
  1. Nan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shoujin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Quan Z. Sheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mehmet A. Orgun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yan Wang.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article belongs to the Topical Collection: Special Issue on Web Information Systems Engineering 2020

Guest Editors: Hua Wang, Zhisheng Huang, and Wouter Beek

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, N., Wang, S., Wang, Y. et al. Exploiting intra- and inter-session dependencies for session-based recommendations. World Wide Web 25, 425–443 (2022). https://doi.org/10.1007/s11280-021-00930-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-021-00930-2

Keywords

Search

Navigation