DEEPER Insight into Your User: Directed Persona Refinement for Dynamic Persona Modeling

TL;DR

DEEPER introduces an iterative reinforcement learning method for dynamic persona modeling, significantly improving user behavior prediction accuracy by continually optimizing personas based on streaming data.

Contribution

It presents a novel reinforcement learning framework that enhances persona update directions, leading to sustained improvements in dynamic persona quality and prediction accuracy.

Findings

32.2% reduction in user behavior prediction error

Outperforms baseline by 22.92%

Effective across 10 domains with 4800 users

Abstract

To advance personalized applications such as recommendation systems and user behavior prediction, recent research increasingly adopts large language models (LLMs) for human -readable persona modeling. In dynamic real -world scenarios, effective persona modeling necessitates leveraging streaming behavior data to continually optimize user personas. However, existing methods -whether regenerating personas or incrementally extending them with new behaviors -often fail to achieve sustained improvements in persona quality or future behavior prediction accuracy. To address this, we propose DEEPER, a novel approach for dynamic persona modeling that enables continual persona optimization. Specifically, we enhance the model's direction -search capability through an iterative reinforcement learning framework, allowing it to automatically identify effective update directions and optimize personas using discrepancies between user behaviors and model predictions. Extensive experiments on dynamic persona modeling involving 4800 users across 10 domains highlight the superior persona optimization capabilities of DEEPER, delivering an impressive 32.2% average reduction in user behavior prediction error over four update rounds -outperforming the best baseline by a remarkable 22.92%.