Skip to main content
Springer Nature Link
Log in

Multi-time-scale with clockwork recurrent neural network modeling for sequential recommendation

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

Abstract

Sequential recommendation (SR) methods improve accuracy by considering the temporal sequence of user-item interactions rather than treating interaction histories as static sets. In this paper, we address the problem of implicit representation learning for multi-time-scale user interests by proposing a novel method based on user behavior sequence modeling—multi-time-scale with clockwork recurrent neural network modeling for sequential recommendation (MTSC). Specifically, firstly, we group the neurons of the hidden layer of the recurrent neural network (RNN) based on the clockwork RNN (CW-RNN) method according to the different degrees of dynamic changes of user interests. Secondly, we design different update frequencies to extract user interest features at multiple time scales. Finally, we model the dependency of user interest features at different time scales through scale-dimensional convolution to generate a unified representation of user interest features at multiple time scales, which can be used to predict the items of interest to users. To validate its effectiveness, we conducted extensive experiments on three public datasets, and the results demonstrate that the MTSC model achieves state-of-the-art performance across all baselines. More precisely, on the Steam dataset, the Precision@10 metric of the MTSC model improved by 4.73% compared to the best baseline model, robustly validating the effectiveness and superiority of the proposed method.

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
Algorithm 1
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

Data will be made available on request.

Notes

  1. https://chaoshi.detail.tmall.com/item.htm?spm=a230r.1.14.41.1f0c73dbSqewru&id=653607486694 &ns=1 &abbucket=1

  2. https://cseweb.ucsd.edu/~jmcauley/datasets.html#steam_data

  3. https://www.kaggle.com/datasets/shivamb/amazon-prime-movies-and-tv-shows

  4. https://webscope.sandbox.yahoo.com/catalog.php?datatype=i&did=67.

References

  1. Rahayu NW, Ferdiana R, Kusumawardani SS (2022) A systematic review of ontology use in e-learning recommender system. Comput Educ: Artif Intell 3:100047

    Google Scholar 

  2. Chen J, Dong H, Wang X, Feng F, Wang M, He X (2023) Bias and debias in recommender system: a survey and future directions. ACM Trans Inf Syst 41(3):1–39

    MATH  Google Scholar 

  3. Li M, Liu Y, Jullien S, Ariannezhad M, Yates A, Aliannejadi M, de Rijke M (2024) Are we really achieving better beyond-accuracy performance in next basket recommendation?, In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 924–934

  4. Cheng Y, Fan Y, Wang Y, Li X (2024) Accurate multi-interest modeling for sequential recommendation with attention and distillation capsule network. Expert Syst Appl 243:122887

    Article  MATH  Google Scholar 

  5. Chen Q, Zhao H, Li W, Huang P, Ou W (2019) Behavior sequence transformer for e-commerce recommendation in alibaba, In: Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data, pp. 1–4

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

  7. Zheng L, Chai H, Chen X, Jin J, Zhang W, Yu Y, Guo X, Ge C, Feng Z (2024) Search-based time-aware graph-enhanced recommendation with sequential behavior data. ACM Trans Recomm Syst 2(4):1–29

    Article  MATH  Google Scholar 

  8. Zhang S, Tay Y, Yao L, Sun A, An J (2019) Next item recommendation with self-attentive metric learning, In: Thirty-Third AAAI Conference on Artificial Intelligence, 9

  9. Ma H, Xie R, Meng L, Chen X, Zhang X, Lin L, Kang Z (2024) Plug-in diffusion model for sequential recommendation, In: Proceedings of the AAAI Conference on Artificial Intelligence 38:8886–8894

  10. Wang P, Guo J, Lan Y, Xu J, Wan S, Cheng X (2015) Learning hierarchical representation model for nextbasket recommendation, In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 403–412

  11. Guo Q, Sun Z, Zhang J, Theng Y-L (2020) An attentional recurrent neural network for personalized next location recommendation, In: Proceedings of the AAAI Conference on Artificial Intelligence, 34, 83–90

  12. Cui Y, Liu H, Wang Q, Zheng Z, Wang H, Yue Z, Ming Z, Wen M, Feng L, Yao M (2022) Investigation on the ignition delay prediction model of multi-component surrogates based on back propagation (bp) neural network. Combust Flame 237:111852

    Article  MATH  Google Scholar 

  13. Xianduo S, Xin W, Yuyuan S, Xianglin Z, Ying W (2022) Hierarchical recurrent neural networks for graph generation. Inf Sci 589:250–264

    Article  Google Scholar 

  14. Liu Y, Li D, Wan S, Wang F, Dou W, Xu X, Li S, Ma R, Qi L (2022) A long short-term memory-based model for greenhouse climate prediction. Int J Intell Syst 37(1):135–151

    Article  MATH  Google Scholar 

  15. Farah S, Humaira N, Aneela Z, Steffen E et al (2022) Short-term multi-hour ahead country-wide wind power prediction for Germany using gated recurrent unit deep learning. Renew Sustain Energy Rev 167:112700

    Article  Google Scholar 

  16. Khaldi R, El Afia A, Chiheb R, Tabik S (2023) What is the best RNN-cell structure to forecast each time series behavior? Expert Syst Appl 215:119140

    Article  Google Scholar 

  17. Landi F, Baraldi L, Cornia M, Cucchiara R (2021) Working memory connections for LSTM. Neural Netw 144:334–341

    Article  Google Scholar 

  18. Wang C, Ma W, Zhang M, Chen C, Liu Y, Ma S (2020) Toward dynamic user intention: temporal evolutionary effects of item relations in sequential recommendation. ACM Trans Inf Syst (TOIS) 39(2):1–33

    MATH  Google Scholar 

  19. Li J, Li H, He Z, Ma W, Sun P, Zhang M, Ma S (2024) Rechorus2. 0: A modular and task-flexible recommendation library, In: Proceedings of the 18th ACM Conference on Recommender Systems, pp. 454–464

  20. Shi X, Zhang Y, Pujahari A, Mishra SK (2025) When latent features meet side information: a preference relation based graph neural network for collaborative filtering. Expert Syst Appl 260:125423

    Article  Google Scholar 

  21. Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms, In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295

  22. Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2009) BPR: Bayesian personalized ranking from implicit feedback, In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 452–461

  23. Wu B, He X, Wu L, Zhang X, Ye Y (2023) Graph-augmented co-attention model for socio-sequential recommendation. IEEE Trans Syst, Man, Cybern: Syst 53(7):4039–4051

    Article  MATH  Google Scholar 

  24. Hidasi B, Karatzoglou A, Baltrunas L, Tikk D (2015) Session-based recommendations with recurrent neural networks, arXiv preprint arXiv:1511.06939

  25. Kang W-C, McAuley J, Self-attentive sequential recommendation, In: 2018 IEEE International Conference on Data Mining (ICDM). IEEE 2018:197–206

  26. Liu Y, Xia L, Huang C (2024) Selfgnn: self-supervised graph neural networks for sequential recommendation, In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1609–1618

  27. Luo T, Liu Y, Pan SJ (2024) Collaborative sequential recommendations via multi-view GNN-transformers. ACM Trans Inf Syst. https://doi.org/10.1145/3649436

    Article  MATH  Google Scholar 

  28. Zheng X, Li X, Chen Z, Sun L, Yu Q, Guo L, Luo Y (2024) Enhanced self-attention mechanism for long and short term sequential recommendation models. IEEE Trans Emerg Top Comput Intell. https://doi.org/10.1109/TETCI.2024.3366771

    Article  MATH  Google Scholar 

  29. Gao J, Zhao X, Li M, Zhao M, Wu R, Guo R, Liu Y, Yin D (2024) Smlp4rec: an efficient all-MLP architecture for sequential recommendations. ACM Trans Inf Syst 42(3):1–23

    Article  MATH  Google Scholar 

  30. Yu L, Zhang C, Liang S, Zhang X (2019) Multi-order attentive ranking model for sequential recommendation, In: Proceedings of the AAAI Conference on Artificial Intelligence, 33, 5709–5716

  31. Huang N, Hu R, Wang X, Ding H (2023) Multi-scale modeling temporal hierarchical attention for sequential recommendation. Inf Sci 641:119126

    Article  Google Scholar 

  32. Hou Y, He Z, McAuley J, Zhao WX (2023) Learning vector-quantized item representation for transferable sequential recommenders, In: Proceedings of the ACM Web Conference, pp. 1162–1171

  33. He W, Xiao Y, Li T, Wang R, Li Q (2023) Interest HD: An interest frame model for recommendation based on HD image generation. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2023.3278673

    Article  MATH  Google Scholar 

  34. Liu Z, Hou Y, McAuley J (2024) Multi-behavior generative recommendation, In: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, pp. 1575–1585

  35. Zhang S, Chen L, Wang C, Li S, Xiong H (2024) Temporal graph contrastive learning for sequential recommendation, In: Proceedings of the AAAI Conference on Artificial Intelligence, 38, 9359–9367

  36. Yue Z, Wang Y, He Z, Zeng H, McAuley J, Wang D (2024) Linear recurrent units for sequential recommendation, In: Proceedings of the 17th ACM International Conference on Web Search and Data Mining, pp. 930–938

  37. Huang N, Hu R, Xiong M, Peng X, Ding H, Jia X, Zhang L (2022) Multi-scale interest dynamic hierarchical transformer for sequential recommendation. Neural Comput Appl 34(19):16643–16654

    Article  MATH  Google Scholar 

  38. Tang J, Wang K (2018) 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

  39. Huang N, Hu R, Wang X, Ding H, Huang X (2023) Cross-platform sequential recommendation with sharing item-level relevance data. Inf Sci 621:265–286

    Article  MATH  Google Scholar 

  40. Koutnik J, Greff K, Gomez F, Schmidhuber J (2014) A clockwork RNN, In: International Conference on Machine Learning, PMLR, pp. 1863–1871

  41. Ding H, Sun Y, Wang Z, Huang N, Shen Z, Cui X (2023) RGAN-el: a GAN and ensemble learning-based hybrid approach for imbalanced data classification. Inf Process & Manag 60(2):103235

    Article  Google Scholar 

  42. Ding H, Chen L, Dong L, Fu Z, Cui X (2022) Imbalanced data classification: a KNN and generative adversarial networks-based hybrid approach for intrusion detection. Future Gener Comput Syst 131:240–254

    Article  MATH  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the Zhejiang Provincial Department of Education Research Project Funding under Grant (No. Y202455123), in part by the Hangzhou Dianzi University Research Initiation Grant (No. KYS275624270), in part by the Natural Science Foundation of Hebei Province (No. F2024501021), in part by the Fundamental Research Funds for the Central Universities (No. N2423015), and in part by the National Natural Science Foundation of China supported our research (No. U22A2035). We conducted the numerical calculations on Wuhan University’s Supercomputing Center system. We thank the center for furnishing us with the computational resources.

Author information

Authors and Affiliations

  1. School of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China

    Nana Huang, Pengfei Jiao, Zhidong Zhao & Bin Yang

  2. School of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, 066000, China

    Hongwei Ding

  3. Data Security Governance Zhejiang Engineering Research Center, Hangzhou Dianzi University, Hangzhou, 310018, China

    Nana Huang, Pengfei Jiao, Zhidong Zhao & Bin Yang

  4. School of Cyber Science and Engineering, Wuhan University, Wuhan, 430072, China

    Ruimin Hu

  5. Hangzhou Institute of Technology, Xidian University, Hangzhou, 311231, China

    Qi Zheng

Authors
  1. Nana Huang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Hongwei Ding
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Ruimin Hu
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Pengfei Jiao
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Zhidong Zhao
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Bin Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Qi Zheng
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Nana Huang contributed to the conceptualization, methodology, validation, investigation, data curation, writing—original draft, and writing—review and editing. Hongwei Ding was involved in the conceptualization, validation, investigation, data curation, writing—original draft, and writing—review and editing. Ruimin Hu contributed to the conceptualization, writing—review and editing, formal analysis, and funding acquisition. Pengfei Jiao performed the conceptualization, writing—review and editing, and data curation. Zhidong Zhao assisted in the conceptualization, writing—review and editing, and formal analysis. Bin Yang contributed to the conceptualization and writing—review and editing. Qi Zheng was involved in writing—review and editing.

Corresponding author

Correspondence to Hongwei Ding.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.

Ethical and informed consent for data used

The research conducted for this article strictly adhered to ethical standards by obtaining informed consent from all authors and pertinent stakeholders prior to utilizing the data, reflecting our commitment to upholding the highest ethical and academic standards in the field of applied intelligence.

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

Huang, N., Ding, H., Hu, R. et al. Multi-time-scale with clockwork recurrent neural network modeling for sequential recommendation. J Supercomput 81, 412 (2025). https://doi.org/10.1007/s11227-025-06925-4

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11227-025-06925-4

Keywords