用于鲁棒的大语言模型赋能推荐的检索增强净化器
Retrieval-Augmented Purifier for Robust LLM-Empowered Recommendation
AI总结：
本文提出RETURN框架，通过检索外部协同信号净化被投毒的用户画像，以即插即用方式增强LLM推荐系统鲁棒性，并在三个数据集上验证其有效性。
摘要：
最近，大语言模型（LLM）赋能的推荐系统彻底改变了个性化推荐框架并引起了广泛关注。尽管取得了显著成功，但现有的LLM赋能的RecSys被证明在面对微小扰动时高度脆弱。为了减轻此类漏洞的负面影响，一种潜在的解决方案是利用基于物品-物品共现的协同信号，来净化攻击者插入到用户历史交互中的恶意协同知识。另一方面，由于能够扩展LLM内部不足的知识，检索增强生成（RAG）技术通过引入外部协同知识，为增强LLM赋能推荐系统的鲁棒性提供了前所未有的机会。因此，在本文中，我们提出了一种新颖的框架（RETURN），通过检索外部协同信号来净化被投毒的用户画像，并以即插即用的方式增强LLM赋能的RecSys的鲁棒性。具体而言，提出了检索增强扰动定位，通过从协同物品图中检索外部知识来识别用户历史序列中的潜在扰动。之后，我们进一步检索协同知识，通过使用删除或替换策略来清除扰动，并引入鲁棒的集成推荐策略来生成最终的鲁棒预测。在三个真实世界数据集上的大量实验证明了所提出的RETURN的有效性。
英文摘要：
Recently, Large Language Model (LLM)-empowered recommender systems have revolutionized personalized recommendation frameworks and attracted extensive attention. Despite the remarkable success, existing LLM-empowered RecSys have been demonstrated to be highly vulnerable to minor perturbations. To mitigate the negative impact of such vulnerabilities, one potential solution is to employ collaborative signals based on item-item co-occurrence to purify the malicious collaborative knowledge from the user's historical interactions inserted by attackers. On the other hand, due to the capabilities to expand insufficient internal knowledge of LLMs, Retrieval-Augmented Generation (RAG) techniques provide unprecedented opportunities to enhance the robustness of LLM-empowered recommender systems by introducing external collaborative knowledge. Therefore, in this paper, we propose a novel framework (RETURN) by retrieving external collaborative signals to purify the poisoned user profiles and enhance the robustness of LLM-empowered RecSys in a plug-and-play manner. Specifically, retrieval-augmented perturbation positioning is proposed to identify potential perturbations within the users' historical sequences by retrieving external knowledge from collaborative item graphs. After that, we further retrieve the collaborative knowledge to cleanse the perturbations by using either deletion or replacement strategies and introduce a robust ensemble recommendation strategy to generate final robust predictions. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed RETURN.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
