PY-GREX: An Explainable Group Recommender Systems Toolkit

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A software toolkit for explainable group recommender systems, including several state-of-the-art explainability methods and evaluation metrics.

➡️ Platform live demo

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About

PY-GREX addresses this critical need, offering a modular Python toolkit equipped with multiple state-of-the-art explainability algorithms to facilitate research and development in eXplainable AI (XAI) for Recommender Systems.

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🚀 Features

PY-GREX provides a modular, end-to-end pipeline for explainable group recommendations.

• Recommendation Models:

  • Matrix Factorization:
    • Alternating Least Squares (ALS)
    • Singular Value Decomposition (SVD)
    • Bayesian Personalized Ranking (BPR)
    • Explainable Matrix Factorization (EMF)
  • Neural Networks:
    • Generalized Matrix Factorization (GMF)
    • Multi-Layer Perceptron (MLP)
    • Neural Collaborative Filtering (NCF)
    • Deep Autoencoder
  • Memory-Based:
    • Item-Based K-Nearest Neighbors

• Group Aggregation Strategies:

  • Consensus-Based:
    • Additive Utilitarian
    • Multiplicative Utilitarian
    • Average Satisfaction
  • Majority-Based:
    • Borda Count
    • Plurality Voting
  • Fairness-Oriented:
    • Least Misery
    • Most Pleasure
    • Most Respected Person

• Explanation Methods:

  • Counterfactual:
    • Sliding Window Explainer (Counterfactual Explanations)
  • Rule-Based:
    • EXPGRS (Association Rules Explainer)
  • Local Explainers:
    • LORE4Groups (Local Rule-Based Explanations)

• Evaluation Metrics:

  • Accuracy:
    • Hit Ratio (HR)
    • Normalized Discounted Cumulative Gain (nDCG)
  • Explainability:
    • Model Fidelity
    • Gaussian Intra-List Diversity (GILD)
    • Rule Support and Confidence

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🏁 Getting Started

Installation

You can install PY-GREX directly using pip:

pip install pygrex

This will install all the required dependencies automatically. PY-GREX requires Python 3.11 or higher.

Local Development

If you want to run the project locally for development:

1. Prerequisites:

   • Python 3.11 or higher
   • Git
   • Conda (recommended)

2. Clone the repository:

   git clone https://github.com/toledomateus/pygrex.git
   cd pygrex
   

3. Create and activate a Conda environment:

   conda create -n pygrex python=3.11
   conda activate pygrex
   

4. Install in development mode:

   pip install -e .
   

This will install the package in development mode, allowing you to modify the source code and see the changes immediately without reinstalling.

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🎈 Usage

Running Locally

To run the Streamlit app locally:

1. Install Streamlit:

   pip install streamlit
   

2. Run the app:

   streamlit run Home.py
   

The app will be available at http://localhost:8501

Interactive Web App

The easiest way to use PY-GREX is through the web application. It allows you to:

• Upload or use default data for users, items, and groups
• Select and train a variety of recommendation models
• Generate group recommendations using different aggregation strategies
• Produce and evaluate explanations for the recommendations

Jupyter Notebooks

For detailed examples, check out the notebooks in the notebooks/ directory:

• expgrs_toy_example.ipynb: Demonstrates the EXPGRS rule-based explainer with association rules
• sliding_window_toy_example.ipynb: Shows how to use counterfactual explanations with the Sliding Window method
• lore4groups_toy_example.ipynb: Illustrates local rule-based explanations using LORE4Groups

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🤝 Contributing

Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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📚 Citation

If you use PY-GREX in your research, please cite our paper:

@inproceedings{Toledo2026GREX,
  author    = {Toledo, Mateus and Yera, Raciel and Barranco, Manuel J. and Dutta, Bapi},
  title     = {{GREX}: A Platform for Supporting Explanations in Group Recommender Systems},
  booktitle = {Intelligent Data Engineering and Automated Learning -- {IDEAL} 2025},
  year      = {2026},
  publisher = {Springer Nature Switzerland AG},
  address   = {Cham},
  series    = {Lecture Notes in Computer Science},
  volume    = {16239},
  pages     = {1--13},
  doi       = {10.1007/978-3-032-10489-2_9}
}

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📜 License

This project is licensed under the MIT License. See the LICENSE file for details.