Customer-R1: Personalized Simulation of Human Behaviors via RL-based LLM Agent in Online Shopping
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909853894574080 |
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| author | Wang, Ziyi Lu, Yuxuan Zhang, Yimeng Huang, Jing Wang, Dakuo |
| author_facet | Wang, Ziyi Lu, Yuxuan Zhang, Yimeng Huang, Jing Wang, Dakuo |
| contents | Simulating step-wise human behavior with Large Language Models (LLMs) has become an emerging research direction, enabling applications in various practical domains. While prior methods, including prompting, supervised fine-tuning (SFT), and reinforcement learning (RL), have shown promise in modeling step-wise behavior, they primarily learn a population-level policy without conditioning on a user's persona, yielding generic rather than personalized simulations. In this work, we pose a critical question: how can LLM agents better simulate personalized user behavior? We introduce Customer-R1, an RL-based method for personalized, step-wise user behavior simulation in online shopping environments. Our policy is conditioned on an explicit persona, and we optimize next-step rationale and action generation via action correctness reward signals. Experiments on the OPeRA dataset emonstrate that Customer-R1 not only significantly outperforms prompting and SFT-based baselines in next-action prediction tasks, but also better matches users' action distribution, indicating higher fidelity in personalized behavior simulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_07230 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Customer-R1: Personalized Simulation of Human Behaviors via RL-based LLM Agent in Online Shopping Wang, Ziyi Lu, Yuxuan Zhang, Yimeng Huang, Jing Wang, Dakuo Computation and Language Simulating step-wise human behavior with Large Language Models (LLMs) has become an emerging research direction, enabling applications in various practical domains. While prior methods, including prompting, supervised fine-tuning (SFT), and reinforcement learning (RL), have shown promise in modeling step-wise behavior, they primarily learn a population-level policy without conditioning on a user's persona, yielding generic rather than personalized simulations. In this work, we pose a critical question: how can LLM agents better simulate personalized user behavior? We introduce Customer-R1, an RL-based method for personalized, step-wise user behavior simulation in online shopping environments. Our policy is conditioned on an explicit persona, and we optimize next-step rationale and action generation via action correctness reward signals. Experiments on the OPeRA dataset emonstrate that Customer-R1 not only significantly outperforms prompting and SFT-based baselines in next-action prediction tasks, but also better matches users' action distribution, indicating higher fidelity in personalized behavior simulation. |
| title | Customer-R1: Personalized Simulation of Human Behaviors via RL-based LLM Agent in Online Shopping |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2510.07230 |