Skip to main content
SpringerLink
Log in

Memory-augmented meta-learning framework for session-based target behavior recommendation

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

Session-based recommendation aims to predict the next item to be interacted by a specific type of behavior (e.g., click or purchase) within a session. However, the main challenge comes from the lack of interactions in the target behavior. Despite state-of-the-art approaches aim to alleviate this issue by incorporating auxiliary behaviors through multi-behavior modeling, they are still weak in supporting cold-start recommendation due to the limited generalization ability. Having witnessed the effectiveness of meta-optimized models for few-shot learning, in this paper, we propose a memory-augmented meta-learning framework for session-based target behavior recommendation. It adopts meta-learning to learn well-generalized global sharing initialization parameters for all sessions, and derives personalized local parameters for each session through fine-tuning. Particularly, we first extract multi-behavior characteristics to derive dynamic user intentions within a session. Then we apply soft-clustering in meta-learning based on well-designed memory structures, so that multi-behavior sessions with similar intention could share related knowledge. The experimental results on two datasets show that our MMFSR model effectively outperforms the state-of-the-art methods.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3

Similar content being viewed by others

Notes

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

  2. https://tianchi.aliyun.com/competition

References

  1. Cai, T., Li, J., Mian, A.S., Li, R., Yu, J.X.: Target-aware holistic influence maximization in spatial social networks. TKDE. PP, 1–1 (2020)

  2. Chen, J., Zhong, M., Li, J., Wang, D., Tu, H.: Effective deep attributed network representation learning with topology adapted smoothing. Transactions on Cybernetics. PP, 1–12 (2021)

  3. Dong, M., Yuan, F., Yao, L., Xu, X., Zhu, L.: MAMO: memory-augmented meta-optimization for cold-start recommendation. In: KDD, pp. 688–697 (2020)

  4. Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: ICML, pp. 1126–1135 (2017)

  5. Gao, C., He, X., Gan, D., Chen, X., Feng, F., Li, Y., Chua, T., Jin, D.: Neural multi-task recommendation from multi-behavior data. In: ICDE, pp. 1554–1557 (2019)

  6. Haldar, N., Reynolds, M., Shao, Q., Paris, C., Chen, Y.: Activity location inference of users based on social relationship. World Wide Web. 24, 1–19 (2021)

  7. Han, P., Yang, P., Zhao, P., Shang, S., Liu, Y., Zhou, J., Gao, X., Kalnis, P.: Gcn-mf: disease-gene association identification by graph convolutional networks and matrix factorization. In: SIGKDD, pp. 705–713 (2019)

  8. He, X., Zhang, H., Kan, M., Chua, T.: Fast matrix factorization for online recommendation with implicit feedback. In: SIGIR, pp. 549–558 (2016)

  9. Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. In: ICLR (2016)

  10. Kang, W., McAuley, J.J.: Self-attentive sequential recommendation. In: ICDM, pp. 197–206 (2018)

  11. Lee, H., Im, J., Jang, S., Cho, H., Chung, S.: Melu: Meta-learned user preference estimator for cold-start recommendation. In: KDD, pp. 1073–1082 (2019)

  12. Li, J., Cai, T., Deng, K., Wang, X., Sellis, T., Xia, F.: Community-diversified influence maximization in social networks. Inf. Syst. 92, 101522 (2020)

  13. Li, J., Jing, M., Lu, K., Zhu, L., Yang, Y., Huang, Z.: From zero-shot learning to cold-start recommendation. In: AAAI, pp. 4189–4196 (2019)

  14. Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., Ma, J.: Neural attentive session-based recommendation. In: CIKM, pp. 1419–1428 (2017)

  15. Li, Z., Wang, X., Li, J., Zhang, Q.: Deep attributed network representation learning of complex coupling and interaction. Knowl. Based Syst. 212, 106618 (2021)

  16. Li, Y., Xu, J., Zhao, P., Fang, J., Chen, W., Zhao, L.: Atlrec: An attentional adversarial transfer learning network for cross-domain recommendation. J. Comput. Sci. Technol. 35, 794–808 (2020)

  17. Liu, Q., Zeng, Y., Mokhosi, R., Zhang, H.: STAMP: short-term attention/memory priority model for session-based recommendation. In: KDD, pp. 1831–1839 (2018)

  18. Loni, B., Pagano, R., Larson, M.A., Hanjalic, A.: Bayesian personalized ranking with multi-channel user feedback. In: Recsys, pp. 361–364 (2016)

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

  20. Singh, A.P., Gordon, G.J.: Relational learning via collective matrix factorization. In: KDD, pp. 650–658 (2008)

  21. Song, J., Xu, J., Zhou, R., Chen, L., Li, J., Liu, C.: CBML: A cluster-based meta-learning model for session-based recommendation. In: CIKM, pp. 1713–1722 (2021)

  22. Sun, H., Xu, J., Zheng, K., Zhao, P., Chao, P., Zhou, X.: MFNP: A meta-optimized model for few-shot next POI recommendation. In: IJCAI, pp. 3017–3023 (2021)

  23. Sun, H., Xu, J., Zhou, R., Chen, W., Liu, C.: Hope: a hybrid deep neural model for out-of-town next poi recommendation. World Wide Web. 24, 1–20 (2021)

  24. Tan, Y.K., Xu, X., Liu, Y.: Improved recurrent neural networks for session-based recommendations. In: RecSys, pp. 17–22 (2016)

  25. Tang, L., Long, B., Chen, B., Agarwal, D.: An empirical study on recommendation with multiple types of feedback. In: KDD, pp. 283–292 (2016)

  26. Tanjim, M.M., Su, C., Benjamin, E., Hu, D., Hong, L., McAuley, J.J.: Attentive sequential models of latent intent for next item recommendation. In: WWW, pp. 2528–2534 (2020)

  27. Tuan, T.X., Phuong, T.M.: 3d convolutional networks for session-based recommendation with content features. In: RecSys, pp. 138–146 (2017)

  28. Vartak, M., Thiagarajan, A., Miranda, C., Bratman, J., Larochelle, H.: A meta-learning perspective on cold-start recommendations for items. In: NIPS, pp. 6904–6914 (2017)

  29. Wang, S., Cao, L., Wang, Y., Sheng, Q.Z., Orgun, M.A., Lian, D.: A survey on session-based recommender systems. Comput. Surv. 54, 154–154 (2022)

  30. Wang, W., Zhang, W., Liu, S., Liu, Q., Zhang, B., Lin, L., Zha, H.: Beyond clicks: Modeling multi-relational item graph for session-based target behavior prediction. In: WWW, pp. 3056–3062 (2020)

  31. Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., Tan, T.: Session-based recommendation with graph neural networks. In: AAAI, pp. 346–353 (2019)

  32. 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: IJCAI, pp. 3940–3946 (2019)

  33. Yang, Y., Guan, Z., Li, J., Zhao, W., Cui, J., Wang, Q.: Interpretable and efficient heterogeneous graph convolutional network. TKDE (2021)

  34. Yin, H., Yang, S., Song, X., Liu, W., Li, J.: Deep fusion of multimodal features for social media retweet time prediction. World Wide Web. 24, 1–18(2020)

  35. Yu, R., Gong, Y., He, X., An, B., Zhu, Y., Liu, Q., Ou, W.: Personalized adaptive meta learning for cold-start user preference prediction. CoRR (2020)

  36. Zhang, Y., Liu, Y., Han, P., Miao, C., Cui, L., Li, B., Tang, H.: Learning personalized itemset mapping for cross-domain recommendation. In: IJCAI, pp. 2561–2567 (2020)

  37. Zhang, C., Song, D., Huang, C., Swami, A., Chawla, N.V.: Heterogeneous graph neural network. In: KDD, pp. 793–803 (2019)

  38. Zheng, Y., Liu, S., Li, Z., Wu, S.: Cold-start sequential recommendation via meta learner. CoRR (2020)

  39. Zimdars, A., Chickering, D.M., Meek, C.: Using temporal data for making recommendations. In: UAI, pp. 580–588 (2001)

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant Nos. 61872258, 61802273, Major project of natural science research in Universities of Jiangsu Province under grant number 20KJA520005.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Soochow University, Soochow, China

    Bo Yu, Junhua Fang & Jiajie Xu

  2. Department of Accounting, Dongwu Business School, Soochow University, Soochow, China

    Xiyang Li

  3. Neusoft Corporation, Shenyang, China

    Chong Tai & Wanjun Cheng

Authors
  1. Bo Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiyang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Junhua Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chong Tai
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wanjun Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jiajie Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Xiyang Li or Jiajie Xu.

Ethics declarations

Declarations

We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Additional information

This article belongs to the Topical Collection: Special Issue on Decision Making in Heterogeneous Network Data Scenarios and Applications

Guest Editors: Jianxin Li, Chengfei Liu, Ziyu Guan, and Yinghui Wu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, B., Li, X., Fang, J. et al. Memory-augmented meta-learning framework for session-based target behavior recommendation. World Wide Web 26, 233–251 (2023). https://doi.org/10.1007/s11280-022-01036-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-022-01036-z

Keywords

Search

Navigation