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[2602.22529] Generative Agents Navigating Digital Libraries
Summary
The paper introduces Agent4DL, a simulator for user search behavior in digital libraries, leveraging large language models to generate realistic user interactions.
Why It Matters
This research addresses the challenge of limited datasets on user search patterns in digital libraries, providing a solution that enhances the understanding of user behavior and improves search system design. By simulating diverse user interactions, it paves the way for more effective digital library services.
Key Takeaways
- Agent4DL simulates user search behavior in digital libraries.
- The simulator generates realistic user profiles and search sessions.
- Agent4DL outperforms existing simulators in diversity and context-awareness.
- This innovation helps overcome privacy-related data scarcity in research.
- The findings can improve the design and functionality of digital libraries.
Computer Science > Information Retrieval arXiv:2602.22529 (cs) [Submitted on 26 Feb 2026] Title:Generative Agents Navigating Digital Libraries Authors:Saber Zerhoudi, Michael Granitzer View a PDF of the paper titled Generative Agents Navigating Digital Libraries, by Saber Zerhoudi and 1 other authors View PDF HTML (experimental) Abstract: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. Subjects: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Digital Libraries (cs.DL) Cite as: arXiv:2602.22529 [cs.IR] (or arXiv:2602.22529v...
