A framework for large scale recommendation algorithms.
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EasyRec Introduction


What is EasyRec?


EasyRec is an easy-to-use framework for Recommendation

EasyRec implements state of the art deep learning models used in common recommendation tasks: candidate generation(matching), scoring(ranking), and multi-task learning. It improves the efficiency of generating high performance models by simple configuration and hyper parameter tuning(HPO).


Get Started

Running Platform:


Why EasyRec?

Run everywhere

Diversified input data

Simple to config

  • Flexible feature config and simple model config
  • Efficient and robust feature generation[used in taobao]
  • Nice web interface in development

It is smart

Large scale and easy deployment

  • Support large scale embedding and online learning
  • Many parallel strategies: ParameterServer, Mirrored, MultiWorker
  • Easy deployment to EAS: automatic scaling, easy monitoring
  • Consistency guarantee: train and serving

A variety of models

Easy to customize

Fast vector retrieve





Any contributions you make are greatly appreciated!

  • Please report bugs by submitting a GitHub issue.
  • Please submit contributions using pull requests.
  • please refer to the Development document for more details.



If EasyRec is useful for your research, please cite:

  title={EasyRec: An easy-to-use, extendable and efficient framework for building industrial recommendation systems},
  author={Mengli Cheng and Yue Gao and Guoqiang Liu and Hongsheng Jin and Xiaowen Zhang},



Join Us

  • DingDing Group: 32260796. (EasyRec usage general discussion.)

  • Email Group: [email protected].

Enterprise Service

  • If you need EasyRec enterprise service support, or purchase cloud product services, you can contact us by DingDing Group.



EasyRec is released under Apache License 2.0. Please note that third-party libraries may not have the same license as EasyRec.

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