OpenRec is an open-source and modular library for neural network-inspired recommendation algorithms. Each recommender is modeled as a computational graph that consists of a structured ensemble of reusable modules connected through a set of well-defined interfaces. OpenRec is built to ease the process of extending and adapting state-of-the-art neural recommenders to heterogeneous recommendation scenarios, where different users', items', and contextual data sources need to be incorporated.
For the structure and the design philosophy of OpenRec, please refer to the following paper published in WSDM'18:
Longqi Yang, Eugene Bagdasaryan, Joshua Gruenstein, Cheng-Kang Hsieh, and Deborah Estrin. 2018. OpenRec: A Modular Framework for Extensible and Adaptable Recommendation Algorithms. In Proceedings of WSDM’18, February 5–9, 2018, Marina Del Rey, CA, USA. 
Before installing OpenRec, please install TensorFlow backend (GPU version is recommended).
- Install OpenRec from PyPI (recommended):
pip install openrec- Install OpenRec from source code:
First, clone OpenRec using git:
git clone https://github.com/ylongqi/openrecThen, cd to the OpenRec folder and run the install command:
cd openrec
python setup.py installUse download_dataset.sh script.
@inproceedings{yang2018openrec,
title={OpenRec: A Modular Framework for Extensible and Adaptable Recommendation Algorithms},
author={Yang, Longqi and Bagdasaryan, Eugene and Gruenstein, Joshua and Hsieh, Cheng-Kang and Estrin, Deborah},
booktitle={Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining},
year={2018},
organization={ACM}
}
