Continual Recommender Systems

2025-07-04

AI Summary

This paper addresses the persistent challenge of continual learning in recommender systems under dynamic environments—specifically, the tension among user interest drift, item pool evolution, shifting popularity trends, and forgetting of historical preferences. We propose the first systematic continual learning framework tailored for recommendation. Methodologically, it integrates a stability-plasticity trade-off mechanism, incremental sequence modeling, cold-start item adaptive representation, and online optimization of recommendation metrics (e.g., NDCG, Recall) under streaming feedback. Our contributions are threefold: (1) formalizing core challenges and establishing an evaluation paradigm for continual recommendation; (2) designing a resource-aware, lightweight update strategy enabling real-time industrial deployment; and (3) constructing a comprehensive methodology covering modeling, training, and evaluation, accompanied by an open-source benchmark platform. This work bridges theoretical foundations and practical deployment for continual recommendation, while identifying scalability, fairness, and multi-source heterogeneous stream coordination as key future directions.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Data Mining & Knowledge Management: Recommender Systems
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

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.

Problem

• Address dynamic user interests and item trends in recommender systems
• Balance stability and plasticity per user in continual learning
• Optimize recommendation metrics under streaming feedback constraints

Innovation

• Adapts to dynamic user interests in real-time
• Balances stability and plasticity per user
• Optimizes recommendations under streaming feedback