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 fast adapt to the ever-changing data distribution. The code is released: https://github.com/alibaba/EasyRec.
Cite
CITATION STYLE
Cheng, M., Gao, Y., Liu, G., & Jin, H. S. (2023). EasyRec: An Easy-to-Use, Extendable and Efficient Framework for Building Industrial Recommendation Systems. In Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023 (Vol. 37, pp. 16419–16421). AAAI Press. https://doi.org/10.1609/aaai.v37i13.27065
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.