AAAI 2023technical5 citations
EasyRec: An Easy-to-Use, Extendable and Efficient Framework for Building Industrial Recommendation Systems
Mengli Cheng, Yue Gao, Guoqiang Liu, HongSheng Jin
Abstract
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.
BibTeX
@article{Cheng_Gao_Liu_Jin_2024, title={EasyRec: An Easy-to-Use, Extendable and Efficient Framework for Building Industrial Recommendation Systems}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/27065}, DOI={10.1609/aaai.v37i13.27065}, abstractNote={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.}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Cheng, Mengli and Gao, Yue and Liu, Guoqiang and Jin, HongSheng}, year={2024}, month={Jul.}, pages={16419-16421} }