🤖 AI Summary
Traditional user profiling approaches rely on discriminative models and manual feature engineering, struggling to capture long-tail behaviors and often yielding fragmented, logically inconsistent profiles. This work proposes UserGPT, a novel framework that leverages large language models to transform massive, noisy user behavioral logs into coherent user narratives, enabling holistic personality inference. UserGPT introduces a dual-path paradigm—combining attribute generation and summary generation—and integrates a user behavior simulation engine, a data semanticization module, multi-stage supervised fine-tuning, and a dual-filter grouped relative policy optimization (DF-GRPO) strategy. Evaluated on HPR-Bench, the framework achieves an Avg@10 of 0.7325 for label prediction and an Acc_Ex of 0.7528 for summary generation, while compressing behavioral records by 97.9% without significant loss of critical information.
📝 Abstract
Personalized user understanding from large-scale digital traces remains a fundamental challenge. Traditional user profiling methods rely on discriminative models and manual feature engineering to predict discrete attributes, often producing fragmented and logically inconsistent profiles that generalize poorly to long-tail behaviors. In this work, we study a generative paradigm in which large language models (LLMs) summarize long and noisy behavioral histories into coherent narratives that capture nuanced user evolution. Our experiments show that even strong LLMs remain limited in complex and implicit personalization reasoning.
We propose UserGPT, a framework for improving LLM-based persona understanding through both attribute generation and summary generation. To address the scarcity of real-world behavioral data, we develop a User Behavior Simulation Engine that produces realistic and complex user trajectories. We further introduce a Data-Centric Semantization module that transforms heterogeneous behavioral logs into structured and semantically coherent inputs, reducing noise and sparsity. On top of this pipeline, we design a curriculum-driven post-training strategy that combines multi-stage Supervised Fine-Tuning (SFT) with Dual-Filter Group Relative Policy Optimization (DF-GRPO) to strengthen reasoning over long behavioral histories.
We also construct HPR-Bench, a benchmark for holistic persona reasoning derived from simulated data. On HPR-Bench, UserGPT achieves an Avg@10 score of 0.7325 on tag prediction and an $Acc_{Ex}$ score of 0.7528 on summary generation, while compressing behavioral records by up to 97.9% with critical information preserved. These results demonstrate the effectiveness of UserGPT for holistic persona reasoning and personalized user-agent interaction.