June 4, 2024, 4:53 a.m. | Sichun Luo, Wei Shao, Yuxuan Yao, Jian Xu, Mingyang Liu, Qintong Li, Bowei He, Maolin Wang, Guanzhi Deng, Hanxu Hou, Xinyi Zhang, Linqi Song

cs.CL updates on arXiv.org arxiv.org

arXiv:2406.01363v1 Announce Type: new
Abstract: Nowadays, large language models (LLMs) have been integrated with conventional recommendation models to improve recommendation performance. However, while most of the existing works have focused on improving the model performance, the privacy issue has only received comparatively less attention. In this paper, we review recent advancements in privacy within LLM-based recommendation, categorizing them into privacy attacks and protection mechanisms. Additionally, we highlight several challenges and propose future directions for the community to address these critical …

abstract advances arxiv attention cs.cl cs.ir future however improving issue language language models large language large language models llm llms paper performance privacy recommendation review type while

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