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.

翻译：在快速发展的数字图书馆领域，大型语言模型（LLMs）的进步为模拟用户行为开辟了新的可能性。这一创新解决了数字图书馆研究中长期存在的挑战：由于隐私问题，公开可用的用户搜索模式数据集极为稀缺。在此背景下，我们提出了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能够生成高度仿真的用户画像和动态搜索会话，精准模拟包括查询、点击和停止在内的实际搜索策略，这些行为均根据特定用户画像进行定制。通过与真实用户数据的对比，我们验证了该模拟器在复现真实用户交互方面的准确性。值得注意的是，与SimIIR 2.0等现有用户搜索模拟器相比，Agent4DL展现出更具竞争力的性能，尤其是在生成更多样化且具有上下文感知能力的用户行为方面。