A Survey of Personalization: From RAG to Agent
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915241228500992 |
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| author | Li, Xiaopeng Jia, Pengyue Xu, Derong Wen, Yi Zhang, Yingyi Zhang, Wenlin Wang, Wanyu Wang, Yichao Du, Zhaocheng Li, Xiangyang Liu, Yong Guo, Huifeng Tang, Ruiming Zhao, Xiangyu |
| author_facet | Li, Xiaopeng Jia, Pengyue Xu, Derong Wen, Yi Zhang, Yingyi Zhang, Wenlin Wang, Wanyu Wang, Yichao Du, Zhaocheng Li, Xiangyang Liu, Yong Guo, Huifeng Tang, Ruiming Zhao, Xiangyu |
| contents | Personalization has become an essential capability in modern AI systems, enabling customized interactions that align with individual user preferences, contexts, and goals. Recent research has increasingly concentrated on Retrieval-Augmented Generation (RAG) frameworks and their evolution into more advanced agent-based architectures within personalized settings to enhance user satisfaction. Building on this foundation, this survey systematically examines personalization across the three core stages of RAG: pre-retrieval, retrieval, and generation. Beyond RAG, we further extend its capabilities into the realm of Personalized LLM-based Agents, which enhance traditional RAG systems with agentic functionalities, including user understanding, personalized planning and execution, and dynamic generation. For both personalization in RAG and agent-based personalization, we provide formal definitions, conduct a comprehensive review of recent literature, and summarize key datasets and evaluation metrics. Additionally, we discuss fundamental challenges, limitations, and promising research directions in this evolving field. Relevant papers and resources are continuously updated at https://github.com/Applied-Machine-Learning-Lab/Awesome-Personalized-RAG-Agent. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10147 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Survey of Personalization: From RAG to Agent Li, Xiaopeng Jia, Pengyue Xu, Derong Wen, Yi Zhang, Yingyi Zhang, Wenlin Wang, Wanyu Wang, Yichao Du, Zhaocheng Li, Xiangyang Liu, Yong Guo, Huifeng Tang, Ruiming Zhao, Xiangyu Information Retrieval Personalization has become an essential capability in modern AI systems, enabling customized interactions that align with individual user preferences, contexts, and goals. Recent research has increasingly concentrated on Retrieval-Augmented Generation (RAG) frameworks and their evolution into more advanced agent-based architectures within personalized settings to enhance user satisfaction. Building on this foundation, this survey systematically examines personalization across the three core stages of RAG: pre-retrieval, retrieval, and generation. Beyond RAG, we further extend its capabilities into the realm of Personalized LLM-based Agents, which enhance traditional RAG systems with agentic functionalities, including user understanding, personalized planning and execution, and dynamic generation. For both personalization in RAG and agent-based personalization, we provide formal definitions, conduct a comprehensive review of recent literature, and summarize key datasets and evaluation metrics. Additionally, we discuss fundamental challenges, limitations, and promising research directions in this evolving field. Relevant papers and resources are continuously updated at https://github.com/Applied-Machine-Learning-Lab/Awesome-Personalized-RAG-Agent. |
| title | A Survey of Personalization: From RAG to Agent |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2504.10147 |