EasyRec: An Easy-to-Use, Extendable and Efficient Framework for Building Industrial Recommendation Systems
DOI:
https://doi.org/10.1609/aaai.v37i13.27065Keywords:
Recommedation Framework, Hyperparameter Optimization, Distributed Training, Large ScaleAbstract
We present EasyRec, an easy-to-use, extendable and efficient recommendation framework for building industrial recommendation systems. Our EasyRec framework is superior in the following aspects:first, EasyRec adopts a modular and pluggable design pattern to reduce the efforts to build custom models; second, EasyRec implements hyper-parameter optimization and feature selection algorithms to improve model performance automatically; third, EasyRec applies online learning to adapt to the ever-changing data distribution. The code is released: https://github.com/alibaba/EasyRec.Downloads
Published
2023-09-06
How to Cite
Cheng, M., Gao, Y., Liu, G., & Jin, H. (2023). EasyRec: An Easy-to-Use, Extendable and Efficient Framework for Building Industrial Recommendation Systems. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16419-16421. https://doi.org/10.1609/aaai.v37i13.27065
Issue
Section
Demonstrations