用知识图谱增强的AI助手，能懂用户喜好并推荐更精准内容。
PersonaAgent with GraphRAG: Community-Aware Knowledge Graphs for Personalized LLM
- 基于用户行为构建动态知识图谱，自动提炼个人偏好
- 在LaMP测试中新闻分类准确率提升11.1%，电影标签提升56.1%
- 适合需要个性化推荐与长期记忆的对话系统开发者
我们提出一种基于人物画像的新型语言模型框架，旨在让AI代理能适应个体用户偏好。该代理以用户‘人物画像’（如用户档案或兴趣）为核心，由大语言模型驱动。为增强上下文理解能力，引入知识图谱增强的检索增强生成机制（Graph RAG），构建由LLM生成的文档图索引，并对相关资讯社区进行摘要。个性化提示通过两部分生成：(1) 从知识图谱提取的用户历史行为与偏好摘要；(2) 基于图聚类检测出的全局交互模式。此动态提示工程使代理在保持一致人物特征的同时，融入群体知识。在LaMP基准测试中，本方法将新闻分类F1提升11.1%，电影标签F1提升56.1%，产品评分预测平均绝对误差降低10.4%。代码已公开于https://anonymous.4open.science/r/PersonaAgentwGraphRAG-DE6F。
原文摘要 · Abstract (English)
We propose a novel framework for persona-based language model system, motivated by the need for personalized AI agents that adapt to individual user preferences. In our approach, the agent embodies the user's "persona" (e.g. user profile or taste) and is powered by a large language model (LLM). To enable the agent to leverage rich contextual information, we introduce a Knowledge-Graph-enhanced Retrieval-Augmented Generation (Graph RAG) mechanism that constructs an LLM-derived graph index of relevant documents and summarizes communities of related information. Our framework generates personalized prompts by combining: (1) a summary of the user's historical behaviors and preferences extracted from the knowledge graph, and (2) relevant global interaction patterns identified through graph-based community detection. This dynamic prompt engineering approach allows the agent to maintain consistent persona-aligned behaviors while benefiting from collective knowledge. On the LaMP benchmark, our method improves news categorization F1 by 11.1%, movie tagging F1 by 56.1%, and reduces product rating MAE by 10.4% over prior methods. Our code is available at https://anonymous.4open.science/r/PersonaAgentwGraphRAG-DE6F
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