DeepRec: An Open-source Toolkit for Deep Learning based Recommendation
This toolkit addresses a practical problem for researchers and practitioners in recommendation systems by providing a unified framework, though it is incremental as it consolidates existing methods rather than introducing new ones.
The paper tackles the challenge of reproducing and comparing deep learning-based recommendation models by releasing DeepRec, an open-source toolkit that implements various algorithms in Python and TensorFlow, covering rating prediction, top-N recommendation, and sequential recommendation scenarios.
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: \textbf{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: \url{https://github.com/cheungdaven/DeepRec}