Awesome
<div align=center> <!-- <h1>Avatar: Agent-based Virtual Approach to Large Scale Recommendation Simulation</h1> --> <h1>On Generative Agents in Recommendation</h1> <img src="https://img.shields.io/badge/License-MIT-blue" alt="license">
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.
</div> <p id="Catalogue"></p>📋 Catalogue
<div> <img src="assets/agent4rec.png" height=40"> </div> <!-- - [Explore Unsolved Problems in Recommender Systems](#Explore-Unsolved-Problems-in-Recommender-Systems) - [Filter Bubble](#Filter-Bubble) - [Causal Discovery](#Causal-Discovery) --> <p id="Preparations"></p>⚙️ 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:
pip install -r requirements.txt
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.
python setup.py build_ext --inplace
The command will install necessary tools for accelerating recommender evaluation.
<!-- Then, run the following command to train a 2 layer LightGCN with bpr loss: ```bash python train_recommender.py --neg_sample 1 --infonce 0 ``` --> <p id="Simulation"></p>⌛️ Simulation
Make sure you are in the main directory (where main.py can be found).
Export your OpenAI API key first:
export OPENAI_API_KEY=<Your OpenAI API key>
Replace <Your OpenAI API key> with your own OpenAI API key.
<p id="Quick-Start"></p>Quick Start
By running the following command, you will start a toy simulation with 3 agents.
python main.py
The response of agents to recommended items will be printed in the terminal. This simulation will take around 3 minutes to finish.
<p id="Explore-Various-Recommender-Settings"></p>Explore Various Recommender Settings
Agent4Rec supports various recommendation systems and different simulation configurations.
python main.py --simulation_name MyExp --modeltype MF --n_avatars 10 --max_pages 5 --items_per_page 4 --execution_mode parallel
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 <model_name> in the command. You can replace <model_name> 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.MultVAE: Pretrained MultVAE model.LightGCN: Pretrained LightGCN model with BPR loss.
See the Results
The results of the simulation will be saved in storage/ml-1m/<model_name>/<experiment_name> directory. As for the command in Section Explore Various Recommender Settings, the results will be saved in storage/ml-1m/MyExp directory. All the interaction history of agent 0 is documented in storage/ml-1m/MF/MyExp/running_logs/0.txt
💰 Simulation Cost
🛎️ Note that all the experiments are powered by ChatGPT-3.5, and a complete simulation involving 1000 users would cost approximately $16. ($0.016/User)