# Continual Recommender Systems

arXiv: https://arxiv.org/abs/2507.03861 · HTML: https://mlsanity.com/paper/2507.03861/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 25 references that are in the map.
- First posted on arXiv 5 Jul 2025.

## Builds on (most cited first, 17 of 17)

- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [Towards Long-term Fairness in Recommendation](https://mlsanity.com/paper/2101.03584/) (2021)
- [Controlling Fairness and Bias in Dynamic Learning-to-Rank](https://mlsanity.com/paper/2005.14713/) (2020)
- [GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems](https://mlsanity.com/paper/2008.13517/) (2020)
- [Ensuring User-side Fairness in Dynamic Recommender Systems](https://mlsanity.com/paper/2308.15651/) (2023)
- [ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendation](https://mlsanity.com/paper/2007.12000/) (2020)
- [LinRec: Linear Attention Mechanism for Long-term Sequential Recommender Systems](https://mlsanity.com/paper/2411.01537/) (2024)
- [ReLoop: A Self-Correction Continual Learning Loop for Recommender Systems](https://mlsanity.com/paper/2204.11165/) (2022)
- [Distillation from Heterogeneous Models for Top-K Recommendation](https://mlsanity.com/paper/2303.01130/) (2023)
- [Structure Aware Incremental Learning with Personalized Imitation Weights for Recommender Systems](https://mlsanity.com/paper/2305.01204/) (2023)
- [Preliminary Study on Incremental Learning for Large Language Model-based Recommender Systems](https://mlsanity.com/paper/2312.15599/) (2023)
- [Bridging Search and Recommendation in Generative Retrieval: Does One Task Help the Other?](https://mlsanity.com/paper/2410.16823/) (2024)
- [D2K: Turning Historical Data into Retrievable Knowledge for Recommender Systems](https://mlsanity.com/paper/2401.11478/) (2024)
- [Continual Collaborative Distillation for Recommender System](https://mlsanity.com/paper/2405.19046/) (2024)
- [ReLoop2: Building Self-Adaptive Recommendation Models via Responsive Error Compensation Loop](https://mlsanity.com/paper/2306.08808/) (2023)
- [Unified Generative Search and Recommendation](https://mlsanity.com/paper/2504.05730/) (2025)
- [Enhancing New-item Fairness in Dynamic Recommender Systems](https://mlsanity.com/paper/2504.21362/) (2025)

## Built on by (most cited first, 2 of 2)

- [Continual Low-Rank Adapters for LLM-based Generative Recommender Systems](https://mlsanity.com/paper/2510.25093/) (2025)
- [Self-Evolving Memory for Generative Recommendation](https://mlsanity.com/paper/2609.15598/) (2026)

## Abstract (arXiv)

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.