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Agent4Rec
Recommender system simulator with 1,000 agents
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Official descriptionAvatar: Agent-based Virtual Approach to Large Scale Recommendation Simulation On Generative Agents in Recommendation Agent4Rec, a recommender system simulator with 1,000 LLM-empowered generative agents.
- Source
- collected from the e2b-dev/awesome-ai-agents list · type: open-source project
- Harvest history
- first seen 2026-10-16 · last seen 2026-10-16 · seen 3× · listed by 2 sources
- Score
- score 203 · 2 sources · repo
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Avatar: Agent-based Virtual Approach to Large Scale Recommendation Simulation --
以下摘录自该仓库的 README（片段） · 在 GitHub 读完整 README →
On Generative Agents in Recommendation
Agent4Rec, a recommender system simulator with 1,000 LLM-empowered generative agents. These agents are initialized from the MovieLens-1M dataset, embodying varied social traits and preferences. Each agent interacts with personalized movie recommendations in a page-by-page manner and undertakes various actions such as watching, rating, evaluating, exiting, and interviewing. With Agent4Rec, we would like to explore the potential of LLM-empowered generative agents in simulating the behavior of genuine, independent humans in recommendation environments.
📋 Catalogue
- Catalogue
- Preparations
- Simulation
- Quick Start
- Explore Various Recommender Settings
- See the result
- Simulation Cost
⚙️ Preparations
Step 1. Install requirements.txt
Set up a virtualenv and install the pytorch manually. After that, install all the dependencies listed in the requirements.txt file by running the following command:
Our experiments have been tested on **Python 3.9.12 with PyTorch 1.13.1+cu117**. Python version over 3.10 may lead to some bugs in the package 'reckit'.
Step 2. Set up necessary environments
Make sure you are in the directory of recommenders/ (where setup.py can be found), and run the following code.
The command will install necessary tools for accelerating recommender evaluation.
⌛️ Simulation
Make sure you are in the **main directory** (where main.py can be found).
Export your OpenAI API key first:
Replace \ with **your own OpenAI API key**.
Quick Start
By running the following command, you will start a toy simulation with **3 agents**.
The response of agents to recommended items will be printed in the terminal. This simulation will take around **3 minutes** to finish.
Explore Various Recommender Settings
Agent4Rec supports various recommendation systems and different simulation configurations.
By running this code, you will start a simulation named MyExp with 10 agents, each agent will browse max to 5 pages with 4 items on a single page. The recommender used in this example is Matrix Factorization (short for MF). And the experiment will be executed in parallel model to speed up the simulation.
You can choose the employed recommender by modifying --modeltype in the command. You can replace with the following supported recommenders:
- Random: Randomly recommend items to users.
- Pop: Randomly recommend popular items to users.
- MF: Pretrained Matrix Factorization model with BPR loss.
Agent4Rec, a recommender system simulator with 1,000 LLM-empowered generative agents. These agents are initialized from the MovieLens-1M dataset, embodying varied social traits and preferences. Each agent interacts with personalized movie recommendations in a page-by-page manner and undertakes various actions such as watching, rating, evaluating, exiting, and interviewing. With Agent4Rec, we would like to explore the potential of LLM-empowered generative agents in simulating the behavior of genuine, independent humans in recommendation environments.
📋 Catalogue
- Catalogue
- Preparations
- Simulation
- Quick Start
- Explore Various Recommender Settings
- See the result
- Simulation Cost
⚙️ Preparations
Step 1. Install requirements.txt
Set up a virtualenv and install the pytorch manually. After that, install all the dependencies listed in the requirements.txt file by running the following command:
Our experiments have been tested on **Python 3.9.12 with PyTorch 1.13.1+cu117**. Python version over 3.10 may lead to some bugs in the package 'reckit'.
Step 2. Set up necessary environments
Make sure you are in the directory of recommenders/ (where setup.py can be found), and run the following code.
The command will install necessary tools for accelerating recommender evaluation.
⌛️ Simulation
Make sure you are in the **main directory** (where main.py can be found).
Export your OpenAI API key first:
Replace \ with **your own OpenAI API key**.
Quick Start
By running the following command, you will start a toy simulation with **3 agents**.
The response of agents to recommended items will be printed in the terminal. This simulation will take around **3 minutes** to finish.
Explore Various Recommender Settings
Agent4Rec supports various recommendation systems and different simulation configurations.
By running this code, you will start a simulation named MyExp with 10 agents, each agent will browse max to 5 pages with 4 items on a single page. The recommender used in this example is Matrix Factorization (short for MF). And the experiment will be executed in parallel model to speed up the simulation.
You can choose the employed recommender by modifying --modeltype in the command. You can replace with the following supported recommenders:
- Random: Randomly recommend items to users.
- Pop: Randomly recommend popular items to users.
- MF: Pretrained Matrix Factorization model with BPR loss.
- Source
- awesome · https://github.com/LehengTHU/Agent4Rec
- added
- yesterday