系统梳理从RAG到智能体的个性化技术演进
A Survey of Personalization: From RAG to Agent
- 按预检索、检索、生成三阶段分析RAG个性化方法
- 提出基于大模型智能体的个性化规划与动态生成框架
- 适合关注个性化AI系统设计的研究者与开发者
个性化已成为现代AI系统的核心能力，使交互能匹配用户偏好、上下文与目标。近期研究聚焦于检索增强生成（RAG）框架及其向更先进代理架构的演进，以提升用户满意度。本文系统考察了RAG在三个核心阶段——预检索、检索、生成中的个性化机制。进一步，将RAG能力拓展至基于大语言模型的个性化智能体，引入用户理解、个性化规划与执行、动态生成等代理功能。针对RAG与代理式个性化，本文提供形式化定义，综述最新文献，总结关键数据集与评估指标。同时讨论该领域的根本挑战、局限与未来方向。相关论文与资源持续更新于https://github.com/Applied-Machine-Learning-Lab/Awesome-Personalized-RAG-Agent。
原文摘要 · Abstract (English)
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.
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