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）的显著进步催生了一个有前景的研究方向：利用LLMs作为推荐系统（LLMRec）。LLMRec的有效性源于其固有的开放世界知识与推理能力。它通过基于用户交互数据的指令微调获取推荐能力。然而，为保护用户隐私并优化效用，LLMRec还需有意遗忘特定用户数据，这通常被称为推荐遗忘。在LLM时代，推荐遗忘为LLMRec带来了“低效”与“低效”的双重挑战：现有遗忘方法需更新LLMRec中数十亿参数，成本高且耗时；同时，它们在遗忘过程中常损害模型效用。为此，我们提出E2URec——首个面向LLMRec的高效且有效的遗忘方法。E2URec通过仅更新少量附加LoRA参数提升遗忘效率，并采用教师-学生框架改善遗忘效果，其中维护多个教师网络指导遗忘过程。大量实验表明，E2URec在两个真实数据集上优于最先进基线方法。具体而言，E2URec能在不影响推荐性能的前提下高效遗忘特定数据。源代码见\url{https://github.com/justarter/E2URec}。