Generative Agents Navigating Digital Libraries

• URL: http://arxiv.org/abs/2602.22529v1
• Date: Thu, 26 Feb 2026 02:08:39 GMT
• Title: Generative Agents Navigating Digital Libraries
• Authors: Saber Zerhoudi, Michael Granitzer,
• Abstract summary: Agent4DL is a user search behavior simulator specifically designed for digital library environments.<n>Agent4DL generates realistic user profiles and dynamic search sessions that closely mimic actual search strategies.<n>Our simulator's accuracy in replicating real user interactions has been validated through comparisons with real user data.
• Score: 2.50369129460887
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• UXSim: Towards a Hybrid User Search Simulation [2.50369129460887]
  The true dynamism and personalization inherent in human-computer interaction demand a more integrated approach.<n>This work introduces UXSim, a novel framework that integrates both approaches.
  arXiv  Detail & Related papers  (2026-02-27T18:14:34Z)
• Understanding Usage and Engagement in AI-Powered Scientific Research Tools: The Asta Interaction Dataset [47.98539809308384]
  We analyze the Asta Interaction dataset, a large-scale resource comprising over 200,000 user queries and interaction logs.<n>We characterize query patterns, engagement behaviors, and how usage evolves with experience.<n>We release the anonymized dataset and analysis with a new query taxonomy to inform future designs of real-world AI research assistants.
  arXiv  Detail & Related papers  (2026-02-26T18:40:28Z)
• Leveraging Scene Context with Dual Networks for Sequential User Behavior Modeling [58.72480539725212]
  We propose a novel Dual Sequence Prediction networks (DSPnet) to capture the dynamic interests and interplay between scenes and items for future behavior prediction.<n>DSPnet consists of two parallel networks dedicated to learn users' dynamic interests over items and scenes, and a sequence feature enhancement module to capture the interplay for enhanced future behavior prediction.
  arXiv  Detail & Related papers  (2025-09-30T12:26:57Z)
• Non-Collaborative User Simulators for Tool Agents [12.294827535425414]
  We propose a novel user simulator architecture that simulates four categories of non-collaborative behaviors.<n>Our experiments on MultiWOZ and $tau$-bench reveal significant performance degradation in state-of-the-art tool agents when encountering non-collaborative users.
  arXiv  Detail & Related papers  (2025-09-27T05:06:17Z)
• How Reliable is Your Simulator? Analysis on the Limitations of Current LLM-based User Simulators for Conversational Recommendation [14.646529557978512]
  We analyze the limitations of using Large Language Models in constructing user simulators for Conversational Recommender System. Data leakage, which occurs in conversational history and the user simulator's replies, results in inflated evaluation results. We propose SimpleUserSim, employing a straightforward strategy to guide the topic toward the target items.
  arXiv  Detail & Related papers  (2024-03-25T04:21:06Z)
• USimAgent: Large Language Models for Simulating Search Users [33.17004578463697]
  We introduce a Large Language Models-based user search behavior simulator, USimAgent. The simulator can simulate users' querying, clicking, and stopping behaviors during search. Empirical investigation on a real user behavior dataset shows that the simulator outperforms existing methods in query generation.
  arXiv  Detail & Related papers  (2024-03-14T07:40:54Z)
• BASES: Large-scale Web Search User Simulation with Large Language Model based Agents [108.97507653131917]
  BASES is a novel user simulation framework with large language models (LLMs) Our simulation framework can generate unique user profiles at scale, which subsequently leads to diverse search behaviors. WARRIORS is a new large-scale dataset encompassing web search user behaviors, including both Chinese and English versions.
  arXiv  Detail & Related papers  (2024-02-27T13:44:09Z)
• On Generative Agents in Recommendation [58.42840923200071]
  Agent4Rec is a user simulator in recommendation based on Large Language Models. Each agent interacts with personalized recommender models in a page-by-page manner.
  arXiv  Detail & Related papers  (2023-10-16T06:41:16Z)
• User Behavior Simulation with Large Language Model based Agents [116.74368915420065]
  We propose an LLM-based agent framework and design a sandbox environment to simulate real user behaviors. Based on extensive experiments, we find that the simulated behaviors of our method are very close to the ones of real humans.
  arXiv  Detail & Related papers  (2023-06-05T02:58:35Z)
• Multi-Agent Task-Oriented Dialog Policy Learning with Role-Aware Reward Decomposition [64.06167416127386]
  We propose Multi-Agent Dialog Policy Learning, which regards both the system and the user as the dialog agents. Two agents interact with each other and are jointly learned simultaneously. Results show that our method can successfully build a system policy and a user policy simultaneously.
  arXiv  Detail & Related papers  (2020-04-08T04:51:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.