EasyRec: An Easy-to-Use, Extendable and Efficient Framework for Building Industrial Recommendation Systems

1Citations
Citations of this article
6Readers
Mendeley users who have this article in their library.

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

APA

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.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free