🧩 DataRec: A Python Library for Standardized and Reproducible Data Management in Recommender Systems

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DataRec focuses on the data management phase of recommender systems, promoting standardization, interoperability, and best practices for data filtering, splitting, analysis, and export.

Official repository of the paper:
📄 DataRec: A Python Library for Standardized and Reproducible Data Management in Recommender Systems (SIGIR 2025) doi

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📑 Table of Contents

• Features
• Installation
• Quickstart
• Datasets
• Documentation
• Contributing
• Citation
• Authors and Contributors
• Related Projects
• License

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Features ✨

• Dataset Management: multi-format I/O with dynamic schema specification.
• Reference Datasets: curated, versioned, and traceable datasets.
• Filtering Strategies: widely used user/item interaction filters.
• Splitting Strategies: temporal and random splits for reproducible evaluation.
• Data Characteristics: compute dataset-level statistics (e.g., sparsity, popularity).
• Interoperability: export datasets to external recommendation frameworks.

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Installation

From source (recommended for development)

git clone https://github.com/sisinflab/DataRec.git
cd DataRec
python3.9 -m venv venv
source venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt
# editable mode + optional dependency groups (defined in pyproject.toml)
pip install -e '.[dev,docs]'

Quickstart 🚀

from datarec.datasets import AmazonOffice
from datarec.processing import FilterOutDuplicatedInteractions, UserItemIterativeKCore
from datarec.splitters import RandomHoldOut

# 1️⃣ Load a reference dataset
data = AmazonOffice(version='2014').prepare_and_load()

# 2️⃣ Apply preprocessing filters
data = FilterOutDuplicatedInteractions().run(data)
data = UserItemIterativeKCore(cores=5).run(data)

# 3️⃣ Split into train/validation/test
splitter = RandomHoldOut(test_ratio=0.2, val_ratio=0.1, seed=42)
splits = splitter.run(data)

train, val, test = splits['train'], splits['val'], splits['test']

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Datasets 📊

The complete and up-to-date list of datasets (with metadata and statistics) is available in the documentation:

👉 Datasets Section

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Documentation 📚

Full documentation available at: https://sisinflab.github.io/DataRec/
Includes API reference, guides, tutorials, and dataset overview.

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Contributing 🤝

Contributions are welcome!
To contribute:

1. Create a feature/fix branch.
2. Add tests and documentation updates as needed.
3. Run tests before pushing.
4. Open a pull request describing your changes clearly.

The project also receives updates from a private development repository maintained by SisInfLab.

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Citation 📖

If you use DataRec in your research, please cite our SIGIR 2025 paper:

@inproceedings{DBLP:conf/sigir/MancinoBF0MPN25,
  author       = {Alberto Carlo Maria Mancino and
                  Salvatore Bufi and
                  Angela Di Fazio and
                  Antonio Ferrara and
                  Daniele Malitesta and
                  Claudio Pomo and
                  Tommaso Di Noia},
  title        = {DataRec: {A} Python Library for Standardized and Reproducible Data
                  Management in Recommender Systems},
  booktitle    = {{SIGIR}},
  pages        = {3478--3487},
  publisher    = {{ACM}},
  year         = {2025}
}

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Authors and Contributors 👥

Authors

• Alberto Carlo Maria Mancino (Politecnico di Bari)
• Salvatore Bufi
• Angela Di Fazio
• Daniele Malitesta
• Antonio Ferrara
• Claudio Pomo
• Tommaso Di Noia

Contributors

Alberto C. M. Mancino	Angela Di Fazio	Salvatore Bufi	Giuseppe Fasano
Gianluca Colonna	Maria L. N. De Bonis	Marco Valentini

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Related Projects 🧩

• Ducho — library for multimodal representation learning: https://github.com/sisinflab/Ducho
• D&D4Rec Tutorial (RecSys 2025) — Standard Practices for Data Processing and Multimodal Feature Extraction in Recommendation with DataRec and Ducho:
  https://sites.google.com/view/dd4rec-tutorial/home

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License 📜

Distributed under the MIT License.
See LICENSE.

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Maintained with ❤️ by SisInfLab