Intro

<h3>What is QRec?</h3> <p><img src="https://camo.githubusercontent.com/865509539b39013182561d2208f7b47538b705c29143fe1f68fb26ef179401ef/68747470733a2f2f692e6962622e636f2f42736e38434d352f6c6f676f2e706e67" alt="QRec" title="QRec" /></p> <p>QRec is a Python framework for recommender systems (Supported by Python 3.7+ and Tensorflow 1.14+) in which a number of popular and state-of-the-art recommendation models are implemented. QRec has a lightweight architecture and provides user-friendly interfaces. It can facilitate model implementation and evaluation.</p> <hr /> <h3>Features of QRec</h3> <p>As a powerful framework, QRec has some wonderful features summarized as follows: </p> <ul> <li>Cross-platform: QRec can be easily deployed and executed in any platforms, including MS Windows, Linux and Mac OS.</li> <li>Fast execution: QRec is based on Numpy, Tensorflow and some lightweight structures, which make it run fast.</li> <li>Easy configuration: QRec configs recommendation models with configuration files and provides multiple evaluation protocols.</li> <li>Easy expansion: QRec provides a set of well-designed interfaces by which new algorithms can be easily implemented.</li> <li>Modularization: There is no need to change other modules when adding a new algorithm. </li> </ul> <hr /> <h3>Contributors</h3> <p>Founder and principal contributor: @Coder-Yu (Junliang Yu) Other contributors: @DouTong (Tong Dou) @Niki666 (Qianqi Fang) @HuXiLiFeng (Feng Li) @BigPowerZ (Liyuan Zhang) @flyxu Supported by: @AIhongzhi (A/Prof. Hongzhi Yin, The University of Queensland), @mingaoo (A/Prof. Min Gao, Chongqing University)</p> <hr /> <center>Last update: 02/08/2021</center>