# ReLoop: A Self-Correction Continual Learning Loop for Recommender Systems

arXiv: https://arxiv.org/abs/2204.11165 · HTML: https://mlsanity.com/paper/2204.11165/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2022.

## Built on by (most cited first, 13 of 13)

- [Federated Continual Learning via Knowledge Fusion: A Survey](https://mlsanity.com/paper/2312.16475/) (2023)
- [Reinforcement Learning in the Real World: A Survey of Statistical Challenges and Future Directions](https://mlsanity.com/paper/2601.15353/) (2026)
- [Prototype-Aligned Federated Soft-Prompts for Continual Web Personalization](https://mlsanity.com/paper/2603.27678/) (2026)
- [MSSR: Memory-Aware Adaptive Replay for Continual LLM Fine-Tuning](https://mlsanity.com/paper/2603.09892/) (2026)
- [Multi-Epoch learning with Data Augmentation for Deep Click-Through Rate Prediction](https://mlsanity.com/paper/2407.01607/) (2024)
- [Continual Recommender Systems](https://mlsanity.com/paper/2507.03861/) (2025)
- [Better Late Than Never: Formulating and Benchmarking Recommendation Editing](https://mlsanity.com/paper/2406.04553/) (2024)
- [Federated Continual Recommendation](https://mlsanity.com/paper/2508.04792/) (2025)
- [BEAR: Towards Beam-Search-Aware Optimization for Recommendation with Large Language Models](https://mlsanity.com/paper/2601.22925/) (2026)
- [Confidence Ranking for CTR Prediction](https://mlsanity.com/paper/2307.01206/) (2023)
- [Enhancing knowledge retention for continual learning with domain-specific adapters and features gating](https://mlsanity.com/paper/2504.08613/) (2025)
- [GradMix: Gradient-based Selective Mixup for Robust Data Augmentation in Class-Incremental Learning](https://mlsanity.com/paper/2505.08528/) (2025)
- [Continual Graph Memory for Adaptive Recommendation under Intent Drift](https://mlsanity.com/paper/2609.04651/) (2026)

## Abstract (arXiv)

Deep learning-based recommendation has become a widely adopted technique in various online applications. Typically, a deployed model undergoes frequent re-training to capture users' dynamic behaviors from newly collected interaction logs. However, the current model training process only acquires users' feedbacks as labels, but fail to take into account the errors made in previous recommendations. Inspired by the intuition that humans usually reflect and learn from mistakes, in this paper, we attempt to build a self-correction learning loop (dubbed ReLoop) for recommender systems. In particular, a new customized loss is employed to encourage every new model version to reduce prediction errors over the previous model version during training. Our ReLoop learning framework enables a continual self-correction process in the long run and thus is expected to obtain better performance over existing training strategies. Both offline experiments and an online A/B test have been conducted to validate the effectiveness of ReLoop.