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

Authors

  • Mengli Cheng Alibaba Group
  • Yue Gao Alibaba Group
  • Guoqiang Liu Alibaba Group
  • HongSheng Jin Alibaba Group

DOI:

https://doi.org/10.1609/aaai.v37i13.27065

Keywords:

Recommedation Framework, Hyperparameter Optimization, Distributed Training, Large Scale

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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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