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Python recommendation tools

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LensKit is a set of Python tools for experimenting with and studying recommender systems. It provides support for training, running, and evaluating recommender algorithms in a flexible fashion suitable for research and education.

LensKit for Python (LKPY) is the successor to the Java-based LensKit project.

If you use LensKit for Python in published research, please cite:

Michael D. Ekstrand. 2020. LensKit for Python: Next-Generation Software for Recommender Systems Experiments. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM '20). DOI:10.1145/3340531.3412778. arXiv:1809.03125 [cs.IR].

Installing

To install the current release with Anaconda (recommended):

conda install -c lenskit lenskit

Or you can use pip:

pip install lenskit

To use the latest development version, install directly from GitHub:

pip install -U git+https://github.com/lenskit/lkpy

Then see Getting Started

Developing

To contribute to LensKit, clone or fork the repository, get to work, and submit a pull request. We welcome contributions from anyone; if you are looking for a place to get started, see the [issue tracker][].

Our development workflow is documented in the wiki; the wiki also contains other information on developing LensKit. User-facing documentation is at https://lkpy.lenskit.org.

We recommend using an Anaconda environment for developing LensKit. To set this up, run:

pip install flit_core packaging
python build-tools/flit-conda.py --create-env --python-version 3.8

This will create a Conda environment called lkpy with the packages required to develop and test LensKit.

We don't maintain the Conda environment specification directly - instead, we maintain information in setup.toml to be able to generate it, so that we define dependencies and versions in one place. The flit-conda package uses Flit's configuration parser to load this data and generate Conda environment files.

Resources

Acknowledgements

This material is based upon work supported by the National Science Foundation under Grant No. IIS 17-51278. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.