Related papers: Continual Recommender Systems

Continual Recommender Systems

URL: http://arxiv.org/abs/2507.03861v1
Date: Sat, 05 Jul 2025 02:20:15 GMT
Title: Continual Recommender Systems
Authors: Hyunsik Yoo, SeongKu Kang, Hanghang Tong,
Abstract summary: Current tutorials on machine learning do not address recommendation-specific demands.<n>We begin by reviewing the background and problem settings, followed by a comprehensive overview of existing approaches.<n>We then highlight recent efforts to apply continual learning to practical deployment environments.
Score: 47.467562063027195
License: http://creativecommons.org/licenses/by/4.0/
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 (e.g., 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.

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