IDEA Lab
Menu
ResearchPeoplePublicationTeachingEventsJoin us
Tutorial · DASFAA 2026

Continual Recommender Systems: A Focus on LLMs and Evolving Trends

DASFAA 2026 · April 27, 2026

DASFAA 2026

Continual Recommender Systems: A Focus on LLMs and Evolving Trends

April 27, 2026

Half day · Approximately 3 hours including short breaks

DASFAA 2026 tutorial

Abstract

Modern recommender systems operate in uniquely dynamic settings: user interests, item pools, and popularity trends shift continuously, and models must adapt in real time without forgetting past preferences.

While existing tutorials on continual or lifelong learning cover broad machine learning domains such as vision and graphs, they do not address recommendation-specific demands—such as balancing stability and plasticity per user, handling cold-start items, and optimizing recommendation metrics under streaming feedback.

This tutorial aims to make a timely contribution by filling that gap. We begin by reviewing the background and problem settings, followed by a comprehensive overview of existing approaches. We then highlight recent efforts to apply continual learning to practical deployment environments, such as resource-constrained systems and sequential interaction settings.

Finally, we discuss open challenges and future research directions. We expect this tutorial to benefit researchers and practitioners in recommender systems, data mining, AI, and information retrieval across academia and industry.

Outline and Timeline

Half day · Approximately 3 hours including short breaks
01

Part I: Introduction and Background

30 minutes
  • Problem definitions and settings
  • Key challenges
  • Applications and use cases
02

Part II: Experience-Replay-based Methods

35 minutes
  • Sample selection for experience replay
  • Replay-based model enhancement
03

Part III: Regularization-based Methods

35 minutes
  • What knowledge to regularize
  • Which temporal knowledge to regularize
  • Personalization of regularization
04

Part IV: Beyond Traditional Settings

35 minutes
  • Resource-constrained environments
  • Sequential interaction environments
05

Part V: Open Challenges and Future Directions

35 minutes
  • Trustworthiness (e.g., fairness, explainability, robustness)
  • Adaptation to foundational models
  • Unified models for recommendation and search

Speakers

Seunghan Lee

Seunghan Lee

Seunghan Lee is a first-year Master’s student in the Department of Computer Science and Engineering at Korea University. His research focuses on learning from heterogeneous information in recommender systems, encompassing multi-modal content and complex user behaviors, as well as continual recommender systems. His work has been published in major conferences, including CIKM.

Seunghyun Baek

Seunghyun Baek

Seunghyun Baek is a first-year Master’s student in the Department of Computer Science and Engineering at Korea University. He has worked on designing a continually updated multi-stage pipeline for recommender systems. His research interests lie in LLM-based recommendation systems and continual learning for recommendation.

Dojun Hwang

Dojun Hwang

Dojun Hwang is currently a final-year B.S. student in the Department of Computer Science and Engineering at Korea University. He has worked on designing recommender systems, especially large language models as a re-ranker. His research interests are large language models for recommendation and information retrieval.

Hyunsik Yoo

Hyunsik Yoo is a fourth-year Ph.D. student in the Siebel School of Computing and Data Science at the University of Illinois Urbana-Champaign. His research focuses on developing data mining and machine learning techniques for recommender systems and graph mining models that are adaptive, trustworthy, and user-inclusive.

His work has been published in major conferences, including KDD, SIGIR, TheWebConf, WSDM, and ICML. He has also served as a program committee member or reviewer for venues such as KDD, CIKM, TheWebConf Companion, AAAI, NeurIPS, DSAA, and TIST.

SeongKu Kang

SeongKu Kang

SeongKu Kang is an Assistant Professor in the Department of Computer Science and Engineering at Korea University. Prior to that, he was a postdoctoral researcher at the University of Illinois Urbana-Champaign. His research interests lie in data mining, recommender systems, and information retrieval.

He has published more than 30 papers in major conferences such as KDD, TheWebConf, CIKM, SIGIR, and EMNLP. He received the Stars of Tomorrow Award from Microsoft Research Asia in 2023, and his paper was selected as a Best Paper at WSDM 2025. He was also recognized as an outstanding reviewer at KDD.

References

16 works

This list includes the prior works covered in this tutorial.

  1. Ahrabian, K., Xu, Y., Zhang, Y., Wu, J., Wang, Y., & Coates, M. (2021). Structure aware experience replay for incremental learning in graph-based recommender systems. CIKM 2021, 2832–2836.
  2. Mi, F., Lin, X., & Faltings, B. (2020). Ader: Adaptively distilled exemplar replay towards continual learning for session-based recommendation. RecSys 2020, 408–413.
  3. Cai, G., Zhu, J., Dai, Q., Dong, Z., He, X., Tang, R., & Zhang, R. (2022). Reloop: A self-correction continual learning loop for recommender systems. SIGIR 2022, 2692–2697.
  4. Zhu, J., Cai, G., Huang, J., Dong, Z., Tang, R., & Zhang, W. (2023). ReLoop2: Building Self-Adaptive Recommendation Models via Responsive Error Compensation Loop. KDD 2023, 5728–5738.
  5. Zhang, X., Chen, Y., Ma, C., Fang, Y., & King, I. (2024). Influential exemplar replay for incremental learning in recommender systems. AAAI 2024, 9368–9376.
  6. Qin, J., Liu, W., Zhang, W., & Yu, Y. (2025). D2K: Turning historical data into retrievable knowledge for recommender systems. TheWebConf 2025, 472–482.
  7. Wang, Y., Zhang, Y., Valkanas, A., Tang, R., Ma, C., Hao, J., & Coates, M. (2023). Structure aware incremental learning with personalized imitation weights for recommender systems. AAAI 2023, 4711–4719.
  8. Xu, Y., et al. (2020). GraphSAIL: Graph structure aware incremental learning for recommender systems. CIKM 2020.
  9. Wang, Y., Zhang, Y., & Coates, M. (2021). Graph structure aware contrastive knowledge distillation for incremental learning in recommender systems. CIKM 2021, 3518–3522.
  10. Yoo, H., Kang, S., Qiu, R., Xu, C., Wang, F., & Tong, H. (2025). Embracing plasticity: Balancing stability and plasticity in continual recommender systems. SIGIR 2025.
  11. Lee, G., Kang, S., Kweon, W., & Yu, H. (2024). Continual Collaborative Distillation for Recommender System. KDD 2024, 1495–1505.
  12. Lee, G., Yoo, H., Hwang, J., Kang, S., & Yu, H. (2025). Leveraging Historical and Current Interests for Continual Sequential Recommendation. arXiv:2506.07466.
  13. Liu, L., Cai, L., Zhang, C., Zhao, X., Gao, J., Wang, W., et al. (2023). LinRec: Linear attention mechanism for long-term sequential recommender systems. SIGIR 2023, 289–299.
  14. Yoo, H., Li, T.-W., Kang, S., Liu, Z., Xu, C., Qi, Q., & Tong, H. (2025). Continual low-rank adapters for LLM-based generative recommender systems. arXiv:2510.25093.
  15. Chen, H., Razin, N., Narasimhan, K., & Chen, D. (2025). Retaining by doing: The role of on-policy data in mitigating forgetting. arXiv:2510.18874.
  16. Lai, S., Zhao, H., Feng, R., Ma, C., Liu, W., Zhao, H., et al. (2025). Reinforcement fine-tuning naturally mitigates forgetting in continual post-training. arXiv:2507.05386.