AGENTIC-SYSTEMSApril 30, 20261 min read7 views

Hierarchical Multi-Persona Induction from User Behavioral Logs: Learning Evidence-Grounded and Truthful Personas

Researchers propose a new hierarchical framework to induce multi-faceted and truthful user personas from behavioral logs. By leveraging a groupwise extension of Direct Preference Optimization (DPO), the method ensures personas are evidence-grounded and significantly improves future interaction predictions.

Computer Science > Artificial Intelligence

Title:Hierarchical Multi-Persona Induction from User Behavioral Logs: Learning Evidence-Grounded and Truthful Personas

View PDF HTML (experimental)Abstract:Behavioral logs provide rich signals for user modeling, but are noisy and interleaved across diverse intents. Recent work uses LLMs to generate interpretable natural-language personas from user logs, yet evaluation often emphasizes downstream utility, providing limited assurance of persona quality itself. We propose a hierarchical framework that aggregates user actions into intent memories and induces multiple evidence-grounded personas by clustering and labeling these memories. We formulate persona induction as an optimization problem over persona quality-captured by cluster cohesion, persona-evidence alignment, and persona truthfulness-and train the persona model using a groupwise extension of Direct Preference Optimization (DPO). Experiments on a large-scale service log and two public datasets show that our method induces more coherent, evidence-grounded, and trustworthy personas, while also improving future interaction prediction.

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Source: arXiv cs.AI Recent

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