데이터과학 및 응용 연구실 (Intelligent Data Science and Applications Lab)

We develop recommender systems that learn from user interactions and evolving preferences to deliver accurate, efficient, and personalized recommendations across domains and modalities. 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. 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 is a first-year Master’s student in the Department of Computer Science and Engineering at Korea Univer