---
title: "User Behavior Simulation with Large Language Model based"
description: "The authors present RecAgent, a sandbox where each simulated person is an LLM-driven agent with… 34 references, cited by 59 papers here."
authors: ["Lei Wang", "Jingsen Zhang", "Hao Yang", "Zhiyuan Chen", "Jiakai Tang", "Zeyu Zhang", "Xu Chen", "Yankai Lin", "Ruihua Song", "Wayne Xin Zhao", "Jun Xu", "Zhicheng Dou", "Jun Wang", "Ji-Rong Wen"]
year: 2023
venue: "arXiv:2306.02552"
source: "https://arxiv.org/abs/2306.02552"
doi: "10.48550/arxiv.2306.02552"
arxiv: "2306.02552"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wang-2023-user-behavior-simulation-large-language/"
---
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# User Behavior Simulation with Large Language Model based Agents

**Authors:** [Lei Wang](https://md.research.elamur.ai/free/en/authors/lei-wang/), [Jingsen Zhang](https://md.research.elamur.ai/free/en/authors/jingsen-zhang/), [Hao Yang](https://md.research.elamur.ai/free/en/authors/hao-yang/), [Zhiyuan Chen](https://md.research.elamur.ai/free/en/authors/zhiyuan-chen/), [Jiakai Tang](https://md.research.elamur.ai/free/en/authors/jiakai-tang/), [Zeyu Zhang](https://md.research.elamur.ai/free/en/authors/zeyu-zhang/), [Xu Chen](https://md.research.elamur.ai/free/en/authors/xu-chen/), [Yankai Lin](https://md.research.elamur.ai/free/en/authors/yankai-lin/), [Ruihua Song](https://md.research.elamur.ai/free/en/authors/ruihua-song/), [Wayne Xin Zhao](https://md.research.elamur.ai/free/en/authors/wayne-xin-zhao/), [Jun Xu](https://md.research.elamur.ai/free/en/authors/jun-xu/), [Zhicheng Dou](https://md.research.elamur.ai/free/en/authors/zhicheng-dou/), [Jun Wang](https://md.research.elamur.ai/free/en/authors/jun-wang/) and [Ji-Rong Wen](https://md.research.elamur.ai/free/en/authors/ji-rong-wen/) · **Year:** 2023 · **Venue:** arXiv:2306.02552 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2306.02552) · [PDF](https://arxiv.org/pdf/2306.02552) · [DOI 10.48550/arxiv.2306.02552](https://doi.org/10.48550/arxiv.2306.02552) · [arXiv 2306.02552](https://arxiv.org/abs/2306.02552) · [OpenAlex](https://openalex.org/W4379539687)

**Citations:** 5 citations (OpenAlex, 2026-10-14) · [59](https://md.research.elamur.ai/free/en/papers/wang-2023-user-behavior-simulation-large-language/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Building synthetic user data for recommender systems and social platforms is hard. Earlier simulators reduce human choice to simple scoring functions, need real logs to train, and usually cover only one environment. This work tests whether large language models can act as believable users with little or no real data. The authors present RecAgent, a sandbox where each simulated person is an LLM-driven agent with three parts. A profile module stores identity, personality traits and interests. A memory module is modelled on cognitive neuroscience and has sensory, short-term and long-term layers plus self-reflection. An action module produces behaviour. Agents browse a pluggable movie recommender, chat with each other and post to their friends. They act in rounds, and how often each agent is active follows a Pareto distribution. Researchers can intervene from outside, and real people can join the simulation. Experiments based on Movielens-1M users found the simulated behaviour close to that of real humans. The sandbox also reproduced information cocoons and user conformity and helped explore ways to reduce them. This points toward nearly zero-shot simulation across multiple environments for human-centered AI.

## Key points
- **Simulated behaviours:** The agents perform six action types. Four happen in the recommender (searching, browsing, clicking and requesting the next page). Two are social: one-to-one chatting and one-to-many broadcasting.
- **Activity levels:** Agent activity is drawn from a Pareto (long-tail) distribution, so a few agents are very active and most act rarely. The authors check this fit against real datasets in the supplement.
- **Believability evaluation:** The tests start from 20 users sampled from Movielens-1M. Recommender behaviour is evaluated on discrimination and generation tasks, and chatting/broadcasting is judged separately. Comparisons include baselines such as RecSim.
- **Control features:** The recommendation algorithm can be swapped freely. Agent profiles can be edited during a run, agents can be "interviewed" with questions, and humans can role-play as agents.
- **Social case studies:** The simulator was applied to information cocoons and conformity behaviour. It reproduced both phenomena and helped suggest mitigation strategies.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University College London](https://md.research.elamur.ai/free/en/universities/university-college-london/) — University, London, United Kingdom · 107 works · 22,924 citations
- [Renmin University of China](https://md.research.elamur.ai/free/en/universities/renmin-university-of-china/) — University, Beijing, China · 139 works · 17,752 citations

📚 Shelves (3): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🗄️ Information Systems](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · 1,730 works

## Cite

Cite as: Lei Wang, Jingsen Zhang, Hao Yang, Zhiyuan Chen, Jiakai Tang, Zeyu Zhang, Xu Chen, Yankai Lin, Ruihua Song, Wayne Xin Zhao, Jun Xu, Zhicheng Dou, Jun Wang and Ji-Rong Wen. 2023. User Behavior Simulation with Large Language Model based Agents. arXiv:2306.02552.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{wang2023user,
  title = {{User Behavior Simulation with Large Language Model based Agents}},
  author = {Lei Wang, Jingsen Zhang, Hao Yang, Zhiyuan Chen, Jiakai Tang, Zeyu Zhang, Xu Chen, Yankai Lin, Ruihua Song, Wayne Xin Zhao, Jun Xu, Zhicheng Dou, Jun Wang and Ji-Rong Wen},
  year = {2023},
  doi = {10.48550/arxiv.2306.02552},
  eprint = {2306.02552},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2306.02552}
}
```

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*Updated 2 November 2026*