The significant advancements in large language models (LLMs) give rise to a promising research direction, i.e., leveraging LLMs as recommenders (LLMRec). The efficacy of LLMRec arises from the open-world knowledge and reasoning capabilities inherent in LLMs. LLMRec acquires the recommendation capabilities through instruction tuning based on user interaction data. However, in order to protect user privacy and optimize utility, it is also crucial for LLMRec to intentionally forget specific user data, which is generally referred to as recommendation unlearning. In the era of LLMs, recommendation unlearning poses new challenges for LLMRec in terms of \textit{inefficiency} and \textit{ineffectiveness}. Existing unlearning methods require updating billions of parameters in LLMRec, which is costly and time-consuming. Besides, they always impact the model utility during the unlearning process. To this end, we propose \textbf{E2URec}, the first \underline{E}fficient and \underline{E}ffective \underline{U}nlearning method for LLM\underline{Rec}. Our proposed E2URec enhances the unlearning efficiency by updating only a few additional LoRA parameters, and improves the unlearning effectiveness by employing a teacher-student framework, where we maintain multiple teacher networks to guide the unlearning process. Extensive experiments show that E2URec outperforms state-of-the-art baselines on two real-world datasets. Specifically, E2URec can efficiently forget specific data without affecting recommendation performance. The source code is at \url{https://github.com/justarter/E2URec}.

翻译：大语言模型（LLMs）的显著进展催生了一个前景广阔的研究方向，即利用LLMs作为推荐系统（LLMRec）。LLMRec的有效性源于LLMs所具备的开放世界知识和推理能力。LLMRec通过基于用户交互数据的指令微调来获得推荐能力。然而，为了保护用户隐私并优化系统效用，LLMRec也需要有意识地遗忘特定用户数据，这一过程通常被称为推荐遗忘。在LLMs时代，推荐遗忘为LLMRec带来了\textit{低效性}与\textit{无效性}两方面的新挑战。现有的遗忘方法需要更新LLMRec中数十亿的参数，这带来了高昂的成本与时间消耗。此外，这些方法在遗忘过程中总会影响模型的效用。为此，我们提出了\textbf{E2URec}，这是首个面向LLMRec的\underline{高}效且\underline{有}效的\underline{遗}忘方法。我们提出的E2URec通过仅更新少量额外的LoRA参数来提升遗忘效率，并通过采用一个教师-学生框架来提升遗忘效果——在该框架中，我们维护多个教师网络来指导遗忘过程。大量实验表明，E2URec在两个真实世界数据集上超越了现有最先进的基线方法。具体而言，E2URec能够高效地遗忘特定数据，同时不影响推荐性能。源代码位于\url{https://github.com/justarter/E2URec}。