A Survey of Personalization: From RAG to Agent

2025-04-14

AI Summary

This paper addresses the core challenge of synergistic evolution between Retrieval-Augmented Generation (RAG) and Large Language Model (LLM)-based agents in personalized AI. Methodologically, it proposes a three-stage personalized RAG framework—pre-retrieval, retrieval, and generation—and formally defines, for the first time, a dual-track personalization paradigm unifying RAG and agent architectures within a coherent analytical framework. It characterizes the transition from static retrieval augmentation to dynamic, agent-driven personalization, integrating user modeling, multi-step reasoning planning, LLM fine-tuning, and interpretable evaluation design. The work systematically surveys state-of-the-art approaches, catalogs mainstream datasets and evaluation benchmarks, and identifies key open challenges—including interpretability, long-term memory, and privacy preservation. Concurrently, it maintains an authoritative open-source knowledge repository, providing both theoretical foundations and practical guidance for advancing personalized AI research.

Technology Category

Cognitive Modeling & Cognitive Systems: Agent ArchitecturesMultiagent Systems: Modeling other AgentsData Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAGUser Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendationSemantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Survey personalization in RAG and agent-based AI systems
• Enhance user satisfaction with customized interactions
• Review challenges and research in personalized AI frameworks

Innovation

• Retrieval-Augmented Generation (RAG) frameworks
• Personalized LLM-based Agents
• Dynamic generation and user understanding