Continual Recommender Systems

TL;DR

This paper discusses the challenges and recent approaches in applying continual learning to recommender systems, emphasizing the need for models to adapt in real-time while retaining past preferences.

Contribution

It provides a comprehensive overview of continual learning methods tailored for recommender systems, addressing specific challenges like stability-plasticity balance and cold-start issues.

Findings

Review of existing continual learning approaches for recommendation

Analysis of deployment challenges in resource-constrained environments

Identification of open research questions in continual recommender 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.