Metadata-Version: 2.1
Name: recsyslearn
Version: 1.0.0
Summary: A small library to compute fairness of recommender systems.
Home-page: https://github.com/giuliowaitforitdavide/recsyslearn
Author: Giulio Davide Carparelli
Author-email: giulio.davide.97@gmail.com
License: GNU General Public License v3
Keywords: recsyslearn
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Software Development :: Version Control :: Git
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.7
License-File: LICENSE
License-File: AUTHORS.rst

===========
Recsyslearn
===========


.. image:: https://github.com/giuliowaitforitdavide/recsyslearn/actions/workflows/tests.yml/badge.svg
        :target: https://github.com/giuliowaitforitdavide/recsyslearn/actions/workflows/tests.yml
        :alt: Test Status

.. image:: https://readthedocs.org/projects/recsyslearn/badge/?version=latest
     :target: https://recsyslearn.readthedocs.io/en/latest/?version=latest
     :alt: Documentation Status

.. image:: https://img.shields.io/pypi/v/recsyslearn.svg
        :target: https://pypi.python.org/pypi/recsyslearn
        :alt: Library Version


A small library to compute fairness of recommender systems.


* Free software: GNU General Public License v3
* Documentation: https://recsyslearn.readthedocs.io.


Features
--------

* Compute Novelty of a recommender system based on its recommendations list.
* Compute Coverage of a recommender system based on its recommendations list.
* Compute Entropy of a recommender system based on its recommendations list.
* Compute Kullback-Leibler divergence of a recommender system based on its recommendations list and the wanted target representation.
* Compute Mutual Information of a recommender system based on its recommendations list.
* Segment an implicit or explicit dataset in groups based on the activity of the users or on the popularity of the items.

Known Issues
------------

In this version of the library, the computation of the metrics for cross groups (user and item groups together) has not been implemented yet.

Credits
-------

This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage


=======
History
=======

0.4.0-alpha (2022-06-24)
------------------------

* First release on PyPI.

0.4.1 (2022-06-27)
-------------------

* Fixed novelty formula.

0.5.0 (2022-07-19)
-------------------

* Added a new item segmentation method, which gives a percentage score to the items based on their popularity.
* More accurate docs, with a beautiful theme.

0.5.1 (2022-07-27)
-------------------

* Fixed mantissa problem with the sum of proportion in the segmentation part.
* Improved code readability and tests coverage.

0.6.0 (2022-08-30)
-------------------

* Added accuracy computation with NDCG@k

1.0.0 (2022-12-21)
-------------------

* Added usage example
* Breaking library refactor
* Improved jobs' workflows, linting and other developing stuffs
* Fix typos in docs and functions
