rectorch is a pytorch-based framework for top-N recommendation. It includes several state-of-the-art top-N recommendation approaches implemented in pytorch.

The latest PyPi release contains the following methods.

New methods are coming and are currently available in the dev branch (see Dev branch).

rectorch is available on PyPi and it can be installed using pip

pip3 install rectorch

If you install rectorch by cloning this repository make sure to install all the requirements.

pip3 install -r requirements.txt

rectorch is composed of 7 main modules summarized in the following.

(To be released soon)

We will soon release a series of python notebooks with examples on how to train and evaluate recommendation methods using rectorch.

The full documentation of the rectorch APIs is available at https://makgyver.github.io/rectorch/.

The easiest way to test rectorch is using pytest.

git clone https://github.com/makgyver/rectorch.git
cd rectorch/tests
pytest

You can also check the coverage using coverage. From the tests folder:

coverage run -m pytest  
coverage report -m

rectorch is developed using a test-driven approach. The master branch (i.e., the pypi release) is the up-to-date version of the framework where each module has been fully tested. However, new untested or under development features are available in the dev branch. The dev version of rectorch can be used by cloning the branch.

git clone -b dev https://github.com/makgyver/rectorch.git
cd rectorch
pip3 install -r requirements.txt

The following features/changes will be soon released:

• Inclusion of ADMM_Slim and SVAE in the official release;
• Splitting of the models module in more sub-modules on the basis of the models' characteristics;
• Introduction of a "global" setting/configuration for the framework.

This framework is constantly growing and the implemented methods are chosen on the basis of the need of our research activity. We plan to include as many state-of-the-art methods as soon as we can, but if you have any specific requests feel free to contact us by opening an issue.

If you are using rectorch in your work, please consider citing this repository.

@misc{rectorch,
    author = {Mirko Polato},
    title = {{rectorch: pytorch-based framework for top-N recommendation}},
    year = {2020},
    month = {may},
    doi = {10.5281/zenodo.3841898},
    version = {1.0},
    publisher = {Zenodo},
    url = {https://doi.org/10.5281/zenodo.153841898991}
}

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