# Exact and Efficient Unlearning for Large Language Model-based Recommendation

arXiv: https://arxiv.org/abs/2404.10327 · HTML: https://mlsanity.com/paper/2404.10327/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2024.

## Built on by (most cited first, 17 of 17)

- [Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond](https://mlsanity.com/paper/2410.19744/) (2024)
- [Unified Parameter-Efficient Unlearning for LLMs](https://mlsanity.com/paper/2412.00383/) (2024)
- [Exact Unlearning of Finetuning Data via Model Merging at Scale](https://mlsanity.com/paper/2504.04626/) (2025)
- [LLM4DSR: Leveraging Large Language Model for Denoising Sequential Recommendation](https://mlsanity.com/paper/2408.08208/) (2024)
- [Item-Language Model for Conversational Recommendation](https://mlsanity.com/paper/2406.02844/) (2024)
- [A Survey on Recommendation Unlearning: Fundamentals, Taxonomy, Evaluation, and Open Questions](https://mlsanity.com/paper/2412.12836/) (2024)
- [CURE4Rec: A Benchmark for Recommendation Unlearning with Deeper Influence](https://mlsanity.com/paper/2408.14393/) (2024)
- [CURE:Circuit-Aware Unlearning for LLM-based Recommendation](https://mlsanity.com/paper/2604.04982/) (2026)
- [Reinforced Strategy Optimization for Conversational Recommender Systems via Network-of-Experts](https://mlsanity.com/paper/2509.26093/) (2025)
- [Towards Efficient and Exact Forgetting Services in Pre-Trained-Model-based Continual Learning](https://mlsanity.com/paper/2505.12239/) (2025)
- [Privacy in LLM-based Recommendation: Recent Advances and Future Directions](https://mlsanity.com/paper/2406.01363/) (2024)
- [Sharpness-Aware Parameter Selection for Machine Unlearning](https://mlsanity.com/paper/2504.06398/) (2025)
- [DP2Unlearning: An Efficient and Guaranteed Unlearning Framework for LLMs](https://mlsanity.com/paper/2504.13774/) (2025)
- [Dual-View Inference Attack: Machine Unlearning Amplifies Privacy Exposure](https://mlsanity.com/paper/2512.16126/) (2025)
- [U-CAN: Utility-Aware Contrastive Attenuation for Efficient Unlearning in Generative Recommendation](https://mlsanity.com/paper/2602.23400/) (2026)
- [Instruction Learning Paradigms: A Dual Perspective on White-box and Black-box LLMs](https://mlsanity.com/paper/2506.21573/) (2025)
- [Unlearning Is Not Just Erasing: Temporal Decoupling via Generation Inequality](https://mlsanity.com/paper/2608.23020/) (2026)

## Abstract (arXiv)

The evolving paradigm of Large Language Model-based Recommendation (LLMRec) customizes Large Language Models (LLMs) through parameter-efficient fine-tuning (PEFT) using recommendation data. The inclusion of user data in LLMs raises privacy concerns. To protect users, the unlearning process in LLMRec, specifically removing unusable data (e.g., historical behaviors) from established LLMRec models, becomes crucial. However, existing unlearning methods are insufficient for the unique characteristics of LLM-Rec, mainly due to high computational costs or incomplete data erasure. In this study, we introduce the Adapter Partition and Aggregation (APA) framework for exact and efficient unlearning while maintaining recommendation performance. APA achieves this by establishing distinct adapters for partitioned training data shards and retraining only the adapters impacted by unusable data for unlearning. To preserve recommendation performance and mitigate considerable inference costs, APA employs parameter-level adapter aggregation with sample-adaptive attention for individual testing samples. Extensive experiments substantiate the effectiveness and efficiency of our proposed framework