Simulating high quality user behavior data has always been a fundamental problem in human-centered applications, where the major difficulty originates from the intricate mechanism of human decision process. Recently, substantial evidence have suggested that by learning huge amounts of web knowledge, large language models (LLMs) can achieve human-like intelligence. We believe these models can provide significant opportunities to more believable user behavior simulation. To inspire such direction, we propose an LLM-based agent framework and design a sandbox environment to simulate real user behaviors. Based on extensive experiments, we find that the simulated behaviors of our method are very close to the ones of real humans. Concerning potential applications, we simulate and study two social phenomenons including (1) information cocoons and (2) user conformity behaviors. This research provides novel simulation paradigms for human-centered applications. The introduction video of RecAgent can be accessed through the Baidu Netdisk(Password: 8sjj) or Google Drive.
Figure 1: RecAgent Framework
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[15/2/2024] RecAgent
v3.0is released on arXiv with the following updates:- 🧠 More comprehensive experiments to validate RecAgent's believablity in simulating real human behaviors
- 🔬 More experiments on the potential of RecAgent for studying social phenomena (e.g., information cocoons and user conformity behavior)
- 📖 New insights on RecAgent and updated paper writing
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[9/18/2023] RecAgent
v2.0is released on arXiv with the following updates:- More Agents: from 25 to at most 1000
- Human-like Memory Mechanism
- Comprehensive Experiment
- System-level and Agent-level Intervention
- Human Involved Simulation
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[6/5/2023] RecAgent
v1.0is released on arXiv: User Behavior Simulation with Large Language Model based Agents
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Novel User Simulation Paradigm: RecAgent is a novel user simulation paradigm that combines large language models (LLMs) with user behavior analysis. It is designed to simulate the behavior of real users in a recommender system. It also replicates users' social interactions, including chatting and posting. RecAgent can be used to evaluate the performance of recommender systems and to generate user behavior data for training and testing.
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Human-like Memory Mechanism: RecAgent's memory mechanism mimics human cognitive processes, divided into sensory memory, short-term memory, and long-term memory. Through efficient compression and scoring of observations, it ensures the relevance and timeliness of information. By combining relevant memory with high-level insights, it achieves more realistic and consistent simulation of user behaviors.
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Large Scale Multi-Agent Simulation: RecAgent is designed for robust, large-scale multi-agent parallel simulations. Seamlessly integrated with multiple OpenAI APIs, it support the capacity to simultaneously engage up to 1000 agents in its simulations.
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Multi-dimensional Evaluation: We conducted an evaluation from two perspectives, that of the agent and that of the system. From the perspective of the agent, we assessed the effectiveness of various types of memory and evaluated whether the agent could access information-rich and relevant memories based on different memory structures. From the system's perspective, we focused on the reliability of the user behavior generated by our system and assessed the efficiency of the simulation.
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High Extensibility: RecAgent is designed to support a variety of recommendation algorithms and is equally compatible with multiple LLMs. Both aspects come with interfaces that are easy to customize.
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Real-time Training and Updating: RecAgent features an advanced mechanism for real-time training and updating of recommendation models. This allows for the continuous improvement of recommendations based on the latest user interactions and behaviors within the simulation, ensuring that the recommendation system remains dynamic and adaptive to new patterns and preferences.
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Human-in-the-loop Simulation: Individuals can create their own Agent to role-play and participate in the entire simulation process. Real people can take the same actions as other Agents, such as watching recommended movies, chatting with other Agents, or posting publicly.
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Flexible Intervention and Control: RecAgent provides diverse means to intervene with Agents and monitor the results. An Agent's profile can be freely modified. Moreover, people can intervene with Agents through dialogue. RecAgent also supports saving checkpoints, facilitating retrospective analysis and counterfactual studies.
To install RecAgent, one can follow the following steps:
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Clone the repository:
git clone https://github.com/RUC-GSAI/YuLan-Rec.git
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Install the required dependencies:
pip install -r requirements.txt
Using
pipto installfaissmay report an error. One solution is to useconda install faiss-cpu -c pytorch. -
Set your OpenAI API key in the
config/config.yamlfile.
To start using RecAgent, follow these steps:
Configure the simulation parameters in the config/config.yaml file.
# name of LLM model, 'custom' for custom model
llm: gpt-3.5
# path for item data
item_path: data/item.csv
# path for user data
user_path: data/user.csv
# path for relationship data
relationship_path: data/relationship_1000.csv
# path for save interaction records
interaction_path: data/interaction.csv
# directory name for faiss index
ckpt_path: data/ckpt
index_name: data/faiss_index
# simulator directory name for saving and restoring
simulator_dir: data/simulator
# simulator restoring name
simulator_restore_file_name:
# random seed
seed: 2023
# recommender system model
rec_model: MF
# train epoch number for recommender system model
epoch_num: 10
# batch size for recommender system model
batch_size: 64
# embedding size for recommender system model
embedding_size: 64
# learning rate for recommender system model
lr: 0.001
# number of epochs
round: 50
# number of agents, which cannot exceed the number of user in user.csv
agent_num: 5
# number of items to be recommended in one page
page_size: 5
# temperature for LLM
temperature: 0
# maximum number of tokens for LLM
max_token: 1500
# execution mode, serial or parallel
execution_mode: parallel
# time interval for action of agents. The form is a number + a single letter, and the letter may be s, m, h, d, M, y
interval: 5h
# number of max retries for LLM API
max_retries: 100
# verbose mode
verbose: True
# threshold for agent num
active_agent_threshold: 100
# method for choose agent to be active, random, sample, or marginal
active_method: random
# propability for agent to be active
active_prob: 1
# implementation of agent memory, recagent or GenerativeAgentMemory
agent_memory: recagent
# list of api keys for LLM API
api_keys:
- xxx
# number of random recommendation, 1,3,5
rec_random_k: 0
# number of random friends, 1,3,5
social_random_k: 0The first agent_num users in user.csv will be loaded. Note that the agent_num in config.yaml cannot exceed the total number of users in user.csv.
Run the simulation script:
python -u simulator.py --config_file config/config.yaml --output_file messages.json --log_file simulation.logconfig_file is the path of the configuration file. output_file is the path of the output file, which is a JSON file containing all messages generated during the simulation. log_file set the path of the log file.
We will obtain some output like:
INFO:17349:os.getpid()=17349
INFO:17349:
active_agent_threshold: 100
active_method: random
active_prob: 1
agent_memory: recagent
agent_num: 5
api_keys: ['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']
batch_size: 64
ckpt_path: data/ckpt
embedding_size: 64
epoch_num: 10
execution_mode: parallel
index_name: data/faiss_index
interaction_path: data/interaction.csv
interval: 5h
item_path: data/item.csv
llm: gpt-3.5
log_file: simulation.log
log_name: 17349
lr: 0.001
max_retries: 100
max_token: 1500
output_file: output/message/messages.json
page_size: 5
play_role: False
rec_model: MF
rec_random_k: 0
recagent_memory: recagent
relationship_path: data/relationship_1000.csv
round: 50
seed: 2023
simulator_dir: data/simulator
simulator_restore_file_name: None
social_random_k: 0
temperature: 0
user_path: data/user_1000.csv
verbose: True
Load faiss db from local
INFO:17349:Time for creating 5 agents: 0.0327298641204834
INFO:17349:Simulator loaded.
0%| | 0/50 [00:00<?, ?it/s]
INFO:17349:Round 1
0%| | 0/10 [00:00<?, ?it/s]
INFO:1422158:Sarah Miller enters the recommender system.
INFO:1422158:Sarah Miller is recommended ['<Casper>;;The movie <Casper > is about a friendly ghost named Casper who lives in a haunted mansion with his three mischievous uncles.', '<The Goonies>;;<The Goonies> is a 1985 adventure-comedy film directed by Richard Donner and produced by Steven Spielberg.', '<One False Move>;;<One False Move> is a crime thriller movie released in 1991.', '<What About Bob?>;;<What About Bob?> is a comedy film about a man named Bob Wiley (played by Bill Murray) who has multiple phobias and anxiety disorders.', '<Phantasm III: Lord of the Dead>;;<Phantasm III: Lord of the Dead> is a horror movie that follows the story of Mike, who is on a mission to stop the Tall Man, a supernatural entity who is responsible for the death of his family.'].
INFO:1422158:Sarah Miller watches ['Casper']
Figure 2: RecAgent Interface
Click the Start button to start the simulation. The simulation will run for epoch rounds. You can click the Pause button to pause and the Reset button to reset the simulation. The demo supports both serial and parallel execution modes. The agent's profile, activity status, system data, and log information are all displayed in real-time on the interface.
The item data used in the simulation is from MovieLens-1M.
User profiles and relationships between users are fabricated for simulation purposes. We provide the FAISS index of the item data in directory faiss_index.
| Paper | Date | Description |
|---|---|---|
| Social Simulacra | 08/22 | Introduce Social simulacra to generate and explore realistic social interactions within thousands of community members |
| Generative Agent | 04/23 | Construct a virtual town comprised of 25 autonomous agents |
| RecAgent | 06/23 | Develop a recommendation system simulation environment, where Agents can interact with the recommendation system or engage with other Agents on a social platform |
| S^3 | 07/23 | Create a news event-driven simulation social network, integrating real-world data |
| Williams et al. | 07/23 | Use generative agent to simulate realistic human behavior in epidemics. |
| Agent4Rec | 10/23 | Build a recommendation system simulator based on the MovieLens dataset |
| Li et al. | 10/23 | Introduce a LLM-based approach for macroeconomic simulations with human-like decision-making agents |
| CompeteAI | 10/23 | Investigate competition between restaurant and customer agents in a GPT-4 simulated environment |
| AUCARENA | 10/23 | Evaluate LLM-based agent in auction simulations, highlighting their strategic and resource management skills |
| WarAgent | 11/23 | Utilize LLM-based agent to simulate historical international conflicts |
| UGI | 12/23 | Introduce UGI using LLMs and CityGPT to simulate and address urban complexities through intelligent agent interactions |
RecAgent uses MIT License. All data and code in this project can only be used for academic purposes.
Welcome to cite our paper if you find it helpful.
@misc{wang2024user,
title={User Behavior Simulation with Large Language Model-based Agents},
author={Lei Wang and Jingsen Zhang and Hao Yang and Zhiyuan Chen and Jiakai Tang and Zeyu Zhang and Xu Chen and Yankai Lin and Ruihua Song and Wayne Xin Zhao and Jun Xu and Zhicheng Dou and Jun Wang and Ji-Rong Wen},
year={2024},
eprint={2306.02552},
archivePrefix={arXiv},
primaryClass={cs.IR}
}