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RecPack

RecPack is an experimentation toolkit for top-N recommendation, using implicit feedback data written in Python, with a familiar interface and clear documentation. Its goal is to support researchers who advance the state-of-the-art in top-N recommendation to write reproducible and reusable experiments. RecPack comes with a range of different datasets, recommendation scenarios, state-of-the-art baselines and metrics. Wherever possible, RecPack sets sensible defaults. For example, hyperparameters of all recommendation algorithms included in RecPack are initialized to the best performing settings found in the original experiments. The design of RecPack is heavily inspired by the interface of scikit-learn, a popular Python package for classification, regression and clustering tasks. Data scientists who are familiar with scikit-learn will already have an intuitive understanding of how to work with RecPack. On top of this, RecPack was developed with a production mindset: All contributions are rigorously reviewed and tested. The RecPack maintainers strive to maintain a test coverage of more than ninety percent at all times.

Installation

All released versions of recpack are published on Pypi, and can be installed using:

pip install recpack

Documentation

Documentation and tutorials can be found at https://recpack.froomle.ai

Usage

RecPack provides a framework for experimentation with recommendation algorithms. It comes pre-packed with a number of commonly used evaluation scenarios (scenarios), evaluation metrics (metrics) and state-of-the-art algorithm implementations (algorithms). New algorithms and evaluation scenarios can be added easily, by subclassing the appropriate base classes. A number of lower level data splitters are provided that can be used to build up more complex evaluation scenarios.

Users can choose to use the Pipeline interface or manually connect components for running experiments. The Pipeline interface is recommended for easy comparison between algorithms. For optimal flexibility you should manually connect components.

For details and examples, check out the quickstart documentation

If you use RecPack for research purposes, please cite the paper.

@inproceedings{10.1145/3523227.3551472,
    author = {Michiels, Lien and Verachtert, Robin and Goethals, Bart},
    title = {RecPack: An(Other) Experimentation Toolkit for Top-N Recommendation Using Implicit Feedback Data},
    year = {2022},
    isbn = {9781450392785},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3523227.3551472},
    doi = {10.1145/3523227.3551472},
    booktitle = {Proceedings of the 16th ACM Conference on Recommender Systems},
    pages = {648–651},
    numpages = {4},
    location = {Seattle, WA, USA},
    series = {RecSys '22}
}

License

This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.

A copy of the GNU Affero General Public License should be distributed along with this program. If not, see http://www.gnu.org/licenses/.

Address Froomle: Posthofbrug 6-8, 2600 Antwerpen-Berchem Contact Froomle: robin.verachtert[at]froomle.com