Run recommender algorithms and experiments
Project description
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.
Python LensKit (LKPY) is the successor to the Java-based LensKit project.
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.
We recommend using an Anaconda environment for developing LensKit. To set this up, run:
conda env create -f dev-environment.yml
This will create a Conda environment called lkpy-dev with the packages required to develop and test LensKit.
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.
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