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).
Running Platform:
- Local / MaxCompute / EMR-DataScience / DLC
- TF1.12-1.15 / TF2.x / PAI-TF
- MaxCompute Table
- HDFS files / Hive Table
- OSS files
- CSV files / Parquet files
- Datahub / Kafka Streams
- 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
- EarlyStop / Best Checkpoint Saver
- Hyper Parameter Search / AutoFeatureCross / Knowledge Distillation / Features Selection
- In development: NAS
- 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
- DSSM / MIND / DropoutNet / CoMetricLearningI2I / PDN
- W&D / DeepFM / MultiTower / DCN / FiBiNet / MaskNet / PPNet / CDN
- DIN / BST / CL4SRec
- MMoE / ESMM / DBMTL / AITM / PLE
- HighwayNetwork / CMBF / UNITER
- More models in development
- Support component-based development
- Easy to implement customized models and components
- Not need to care about data pipelines
- Run knn algorithm of vectors in distribute environment
- Home
- FAQ
- EasyRec Framework(PPT)
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:
@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}
}
- DingDing Group: 32260796. (EasyRec usage general discussion.)
- DingDing Group2: 37930014162, click this url or scan QrCode to join
- Email Group: [email protected].
- 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.
