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.
Important
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].
Note
LensKit had significant changes in the 2025.1 release. See the Migration Guide for details.
To install the current release with Anaconda (recommended):
conda install -c conda-forge lenskit
If you use Pixi, you can add it to your project:
pixi add lenskit
Or you can use pip (or uv):
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
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 uv for developing LensKit. Our pyproject.toml file
contains the development dependencies. To set up a development environment:
$ uv venv -p 3.12
$ uv syncIf you want all extras (may not work on Windows), do:
$ uv sync --all-extrasYou can then activate the virtual environment to have the tools available and
run tools like pytest:
$ . ./.venv/bin/activateYou should always test your changes by running the LensKit test suite:
python -m pytest
If you want to use your changes in a LensKit experiment, you can locally install your modified LensKit into your experiment's environment. We recommend using separate environments for LensKit development and for each experiment; you will need to install the modified LensKit into your experiment's repository:
conda activate my-exp
conda install -c conda-forge
cd /path/to/lkpy
pip install -e . --no-deps
You may need to first uninstall LensKit from your experiment repo; make sure that LensKit's dependencies are all still installed.
Once you have pushed your code to a GitHub branch, you can use a Git repository as
a Pip dependency in an environment.yml for your experiment, to keep using the
correct modified version of LensKit until your changes make it in to a release.
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.