Abstract
Deep learning based recommender systems have
been extensively explored in recent years. However, the large number of models proposed each
year poses a big challenge for both researchers
and practitioners in reproducing the results for
further comparisons. Although a portion of papers provides source code, they adopted different
programming languages or different deep learning packages, which also raises the bar in grasping the ideas. To alleviate this problem, we released the open source project: DeepRec. In this
toolkit, we have implemented a number of deep
learning based recommendation algorithms using
Python and the widely used deep learning package - Tensorflow. Three major recommendation
scenarios: rating prediction, top-N recommendation (item ranking) and sequential recommendation, were considered. Meanwhile, DeepRec maintains good modularity and extensibility to easily incorporate new models into the framework. It is distributed under the terms of the GNU General Public License. The source code is available at github:
https://github.com/cheungdaven/DeepRec