# ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendation

arXiv: https://arxiv.org/abs/2007.12000 · HTML: https://mlsanity.com/paper/2007.12000/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2020.

## Built on by (most cited first, 22 of 22)

- [Online Continual Learning in Image Classification: An Empirical Survey](https://mlsanity.com/paper/2101.10423/) (2021)
- [Infinite Recommendation Networks: A Data-Centric Approach](https://mlsanity.com/paper/2206.02626/) (2022)
- [Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2](https://mlsanity.com/paper/2208.05056/) (2022)
- [GPT4Rec: Graph Prompt Tuning for Streaming Recommendation](https://mlsanity.com/paper/2406.08229/) (2024)
- [Multi-Label Continual Learning for the Medical Domain: A Novel Benchmark](https://mlsanity.com/paper/2404.06859/) (2024)
- [Reinforcement Learning in the Real World: A Survey of Statistical Challenges and Future Directions](https://mlsanity.com/paper/2601.15353/) (2026)
- [Augmenting Netflix Search with In-Session Adapted Recommendations](https://mlsanity.com/paper/2206.02254/) (2022)
- [Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation Systems](https://mlsanity.com/paper/2209.01143/) (2022)
- [Global Convergence of Continual Learning on Non-IID Data](https://mlsanity.com/paper/2503.18511/) (2025)
- [Fairness Evolution in Continual Learning for Medical Imaging](https://mlsanity.com/paper/2406.02480/) (2024)
- [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)
- [Efficient Multimodal Streaming Recommendation via Expandable Side Mixture-of-Experts](https://mlsanity.com/paper/2508.05993/) (2025)
- [Continual Low-Rank Adapters for LLM-based Generative Recommender Systems](https://mlsanity.com/paper/2510.25093/) (2025)
- [Continual Learning for CTR Prediction: A Hybrid Approach](https://mlsanity.com/paper/2201.06886/) (2022)
- [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)
- [Toward User Preference Alignment in LLM Recommendation via Explicit Context Feedback](https://mlsanity.com/paper/2605.29141/) (2026)
- [FINED: Feed Instance-Wise Information Need with Essential and Disentangled Parametric Knowledge from the Past](https://mlsanity.com/paper/2406.00012/) (2024)
- [Feature Staleness Aware Incremental Learning for CTR Prediction](https://mlsanity.com/paper/2505.02844/) (2025)
- [MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models](https://mlsanity.com/paper/2509.07319/) (2025)

## Abstract (arXiv)

Session-based recommendation has received growing attention recently due to the increasing privacy concern. Despite the recent success of neural session-based recommenders, they are typically developed in an offline manner using a static dataset. However, recommendation requires continual adaptation to take into account new and obsolete items and users, and requires "continual learning" in real-life applications. In this case, the recommender is updated continually and periodically with new data that arrives in each update cycle, and the updated model needs to provide recommendations for user activities before the next model update. A major challenge for continual learning with neural models is catastrophic forgetting, in which a continually trained model forgets user preference patterns it has learned before. To deal with this challenge, we propose a method called Adaptively Distilled Exemplar Replay (ADER) by periodically replaying previous training samples (i.e., exemplars) to the current model with an adaptive distillation loss. Experiments are conducted based on the state-of-the-art SASRec model using two widely used datasets to benchmark ADER with several well-known continual learning techniques. We empirically demonstrate that ADER consistently outperforms other baselines, and it even outperforms the method using all historical data at every update cycle. This result reveals that ADER is a promising solution to mitigate the catastrophic forgetting issue towards building more realistic and scalable session-based recommenders.