Towards efficient and effective unlearning of large language models for recommendation
摘要
In this letter, we propose E2URec, the efficient and effective unlearning method for LLMRec. Our method enables LLMRec to efficiently forget the specific data by only updating the lightweight LoRA modules. Besides, to enhance the effectiveness, our method develop two teacher models to instruct the unlearned model to forget information without harming the recommendation performance. Extensive experiments show that E2URec outperforms state-of-the-art baselines on two real-world datasets.