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

Local / MaxCompute / EMR-DataScience / DLC
TF1.12-1.15 / TF2.x / PAI-TF

Diversified input data

MaxCompute Table
HDFS files / Hive Table
OSS files
CSV files / Parquet files
Datahub / Kafka Streams

Simple to config

Flexible feature config and simple model config
Build models by combining some components
Efficient and robust feature generation[used in taobao]
Nice web interface in development

It is smart

EarlyStop / Best Checkpoint Saver
Hyper Parameter Search / AutoFeatureCross / Knowledge Distillation / Features Selection
In development: NAS

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

DSSM / MIND / DropoutNet / CoMetricLearningI2I / PDN
W&D / DeepFM / MultiTower / DCN / FiBiNet / MaskNet
DIN / BST
MMoE / ESMM / DBMTL / PLE
CMBF / UNITER
More models in development

Easy to customize

Support component-based development
Easy to implement customized models and components
Not need to care about data pipelines

Fast vector retrieve

Run knn algorithm of vectors in distribute environment

Document

Contribute

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.

Cite

If EasyRec is useful for your research, please cite:

@article{Cheng2022EasyRecAE,
  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},
  journal={ArXiv},
  year={2022},
  volume={abs/2209.12766}
}

Contact

Join Us

DingDing Group: 32260796. (EasyRec usage general discussion.)
Email Group: easy_rec@service.aliyun.com.

Enterprise Service

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

License

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

dotrado/EasyRec