用AI模拟数字图书馆用户搜索行为，解决数据隐私难题。
Generative Agents Navigating Digital Libraries
- 构建可生成真实用户画像与动态搜索会话的AI代理系统
- 在真实数据对比中表现优于现有模拟器，行为更多样且上下文敏感
- 适合数字图书馆研究、信息检索系统评估等场景
在数字图书馆领域快速发展的背景下，大型语言模型（LLMs）为模拟用户行为提供了新可能。这一创新解决了长期困扰该领域的难题：由于隐私顾虑，公开可用的用户搜索模式数据集稀缺。为此，我们提出Agent4DL，一个专为数字图书馆环境设计的用户搜索行为模拟器。Agent4DL能够生成真实的用户画像和动态搜索会话，精准模仿实际查询、点击与停止行为，并根据用户特征定制策略。通过与真实用户数据的对比验证，其交互模拟精度显著，性能优于现有模拟器如SimIIR 2.0，尤其在生成更丰富多样的上下文感知行为方面表现突出。
原文摘要 · Abstract (English)
In the rapidly evolving field of digital libraries, the development of large language models (LLMs) has opened up new possibilities for simulating user behavior. This innovation addresses the longstanding challenge in digital library research: the scarcity of publicly available datasets on user search patterns due to privacy concerns. In this context, we introduce Agent4DL, a user search behavior simulator specifically designed for digital library environments. Agent4DL generates realistic user profiles and dynamic search sessions that closely mimic actual search strategies, including querying, clicking, and stopping behaviors tailored to specific user profiles. Our simulator's accuracy in replicating real user interactions has been validated through comparisons with real user data. Notably, Agent4DL demonstrates competitive performance compared to existing user search simulators such as SimIIR 2.0, particularly in its ability to generate more diverse and context-aware user behaviors.
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