ML Anthology

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

Mengli Cheng, Yue Gao, Guoqiang Liu, Hongsheng Jin
AAAI 2023 pp. 16419-16421
doi:10.1609/AAAI.V37I13.27065 /aaai/2023/cheng2023aaai-easyrec/

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.

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Cite

Text

Cheng et al. "EasyRec: An Easy-to-Use, Extendable and Efficient Framework for Building Industrial Recommendation Systems." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I13.27065

Markdown

[Cheng et al. "EasyRec: An Easy-to-Use, Extendable and Efficient Framework for Building Industrial Recommendation Systems." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/cheng2023aaai-easyrec/) doi:10.1609/AAAI.V37I13.27065

BibTeX

@inproceedings{cheng2023aaai-easyrec,
  title     = {{EasyRec: An Easy-to-Use, Extendable and Efficient Framework for Building Industrial Recommendation Systems}},
  author    = {Cheng, Mengli and Gao, Yue and Liu, Guoqiang and Jin, Hongsheng},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2023},
  pages     = {16419-16421},
  doi       = {10.1609/AAAI.V37I13.27065},
  url       = {https://mlanthology.org/aaai/2023/cheng2023aaai-easyrec/}
}