Python recommendation tools
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].
To install the current release with Anaconda (recommended):
conda install -c lenskit lenskit
Or you can use
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].
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
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.
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.