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
https://github.com/justarter/E2URec.

E2URec 是首个高效和有效的语言模型推荐系统遗忘方法，通过更新参数和采用师生网络架构实现推荐数据的忘记，以优化隐私保护和模型性能。

面向高效和有效的推荐算法大型语言模型去学习

Towards Efficient and Effective Unlearning of Large Language Models for  Recommendation

Recommender systems are important for providing personalized services to
users, but the vast amount of collected user data has raised concerns about
privacy (e.g., sensitive data), security (e.g., malicious data) and utility
(e.g., toxic data). To address these challenges, recommendation unlearning has
emerged as a promising approach, which allows specific data and models to be
forgotten, mitigating the risks of sensitive/malicious/toxic user data.
However, existing methods often struggle to balance completeness, utility, and
efficiency, i.e., compromising one for the other, leading to suboptimal
recommendation unlearning. In this paper, we propose an Interaction and Mapping
Matrices Correction (IMCorrect) method for recommendation unlearning. Firstly,
we reveal that many collaborative filtering (CF) algorithms can be formulated
as mapping-based approach, in which the recommendation results can be obtained
by multiplying the user-item interaction matrix with a mapping matrix. Then,
IMCorrect can achieve efficient recommendation unlearning by correcting the
interaction matrix and enhance the completeness and utility by correcting the
mapping matrix, all without costly model retraining. Unlike existing methods,
IMCorrect is a whitebox model that offers greater flexibility in handling
various recommendation unlearning scenarios. Additionally, it has the unique
capability of incrementally learning from new data, which further enhances its
practicality. We conducted comprehensive experiments to validate the
effectiveness of IMCorrect and the results demonstrate that IMCorrect is
superior in completeness, utility, and efficiency, and is applicable in many
recommendation unlearning scenarios.

推荐系统是为用户提供个性化服务的重要工具，但庞大的用户数据收集引发了隐私（如敏感数据）、安全（如恶意数据）和效用（如有害数据）方面的担忧。本文提出了一种相互作用和映射矩阵修正（IMCorrect）方法用于推荐系统去学习，通过修正相互作用矩阵和映射矩阵来提高完整性和效用，且无需昂贵的模型重训练。IMCorrect 是一种白盒模型，具有处理各种推荐系统去学习场景的灵活性，同时具备从新数据中逐步学习的独特能力，进一步增强其实用性。综合实验验证了 IMCorrect 的有效性，结果表明其在完整性、效用性和效率性方面优于其他方法，并适用于多种推荐系统去学习场景。