fairreckitlib 1.0.1
Library for FairRecKit application
Requires Python: >=3.6

# FairRecKitLib
[![Pylint](https://github.com/FairRecKit/FairRecKitLib/actions/workflows/pylint.yml/badge.svg)](https://github.com/FairRecKit/FairRecKitLib/actions/workflows/pylint.yml)
[![PEP257](https://github.com/FairRecKit/FairRecKitLib/actions/workflows/pydocstyle.yml/badge.svg)](https://github.com/FairRecKit/FairRecKitLib/actions/workflows/pydocstyle.yml)
[![Pytest with Coverage](https://github.com/FairRecKit/FairRecKitLib/actions/workflows/pytest-coverage.yml/badge.svg)](https://github.com/FairRecKit/FairRecKitLib/actions/workflows/pytest-coverage.yml)
[![Upload to PyPI](https://github.com/FairRecKit/FairRecKitLib/actions/workflows/pypi-publish.yml/badge.svg)](https://github.com/FairRecKit/FairRecKitLib/actions/workflows/pypi-publish.yml)
[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
![GitHub release (latest by date)](https://img.shields.io/github/v/release/FairRecKit/FairRecKitLib?label=Release)

FairRecKitLib is a library that functions as a combinatory interface between a set of existing recommender libraries, such as [LensKit](https://pypi.org/project/lenskit/), [Implicit](https://pypi.org/project/implicit/), and [Surprise](https://pypi.org/project/scikit-surprise/). It was made to accompany the [FairRecKit application (FairRecKitApp)](https://github.com/FairRecKit/FairRecKitApp).

This software has been developed by students within the Software Project course of the bachelor program Computer Science at Utrecht University, commissioned by Christine Bauer.

Development team: 
Lennard Chung, 
Aleksej Cornelissen,
Isabelle van Driessel,
Diede van der Hoorn,
Yme de Jong,
Lan Le,
Sanaz Najiyan Tabriz,
Roderick Spaans,
Casper Thijsen,
Robert Verbeeten,
Vos Wesseling,
Fern Wieland    

© Copyright Utrecht University (Department of Information and Computing Sciences)

If you use FairRecKit in research, please cite:
> Christine Bauer, Lennard Chung, Aleksej Cornelissen, Isabelle van Driessel, Diede van der Hoorn, Yme de Jong, Lan Le, Sanaz Najiyan Tabriz, Roderick Spaans, Casper Thijsen, Robert Verbeeten, Vos Wesseling, & Fern Wieland (2023). FairRecKit: A Web-based analysis software for recommender evaluations. Proceedings of the 8th ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR 2023), Austin, TX, USA, 19–23 March, pp 438-443. DOI: [10.1145/3576840.3578274](https://doi.org/10.1145/3576840.3578274)

# Installation Requirements
FairRecKitLib utilises the scikit-surprise package, which relies on having a suitable C/C++ compiler present on the system to be able to install itself. For this purpose, make sure you have [Cython](https://pypi.org/project/Cython/) installed before attempting to install FairRecKitLib. If your system lacks a compiler, install the 'Desktop development with C++' build tools through the [Visual Studio installer](https://aka.ms/vs/17/release/vs_buildtools.exe).

Meeting these requirements, you can install FairRecKitLib like any PyPI package, using e.g. pip or conda.

**pip:**  
`pip install fairreckitlib`

**conda**  
`conda install fairreckitlib`

# Documentation
Please check out the [FairRecKitLib Wiki](https://github.com/FairRecKit/FairRecKitLib/wiki) and [FairRecKitLib API](https://FairRecKit.github.io/FairRecKitLib/src/fairreckitlib) for instructions and guides on how to utilise the library or add new functionality.