.. image:: https://img.shields.io/pypi/v/mrec.svg :target: https://pypi.python.org/pypi/mrec/ .. image:: https://travis-ci.org/Mendeley/mrec.svg?branch=master :target: https://travis-ci.org/Mendeley/mrec
mrec is a Python package developed at
Mendeley <http://www.mendeley.com>_ to support recommender systems development and evaluation. The package currently focuses on item similarity and other methods that work well on implicit feedback, and on experimental evaluation.
Why another package when there are already some really good software projects implementing recommender systems?
mrec tries to fill two small gaps in the current landscape, firstly by supplying
simple tools for consistent and reproducible evaluation, and secondly by offering examples
of how to use IPython.parallel to run the same code either on the cores of a single machine
or on a cluster. The combination of IPython and scientific Python libraries is very powerful,
but there are still rather few examples around that show how to get it to work in practice.
Documentation for mrec can be found at http://mendeley.github.io/mrec.
The source code is available at https://github.com/mendeley/mrec.
mrec implements the SLIM recommender described in _. Please cite this paper if you
mrec in your research.
To use mrec in your Python project:
pip install mrec
To set up the project on your own development machine, follow these steps.
To install the dependencies:
pip install cython numpy scipy.
python setup.py installto obtain the other Python dependencies.
To run the tests:
For more specific project build instructions, please see the .travis.yml config file at the top of this Git repo, which specifies how Travis CI auto-builds and tests our project.
If you have fixed a bug or added a neat new feature, feel free to submit a pull request to us on GitHub.
..  Mark Levy, Kris Jack (2013). Efficient Top-N Recommendation by Linear Regression. In Large Scale Recommender Systems Workshop in RecSys'13. ..  Hu, Y., Koren, Y., & Volinsky, C. (2008). Collaborative filtering for implicit feedback datasets. In IEEE ICDM'08. ..  Weston, J., Bengio, S., & Usunier, N. (2010). Large scale image annotation: learning to rank with joint word-image embeddings. Machine learning, 81(1), 21-35.