PersonaTrace: Synthesizing Realistic Digital Footprints with LLM Agents
Digital footprints (records of individuals' interactions with digital systems) are essential for studying behavior, developing personalized applications, and training machine learning models. However, research in this area is often hindered by the scarcity of diverse and accessible data. To address this limitation, we propose a novel method for synthesizing realistic digital footprints using large language model (LLM) agents. Starting from a structured user profile, our approach generates diverse and plausible sequences of user events, ultimately producing corresponding digital artifacts such as emails, messages, calendar entries, reminders, etc. Intrinsic evaluation results demonstrate t...
| Authors | Minjia Wang, Yunfeng Wang, Xiao Ma et al. |
|---|---|
| Primary category | cs.CL |
| Published | 12 March 2026 |
| Last updated at source | 12 March 2026 |
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