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).
- Local / MaxCompute / EMR-DataScience / DLC
- TF1.12-1.15 / TF2.x / PAI-TF
- MaxCompute Table
- HDFS files
- OSS files
- Kafka Streams
- Local CSV
- Flexible feature config and simple model config
- 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 / MultiModal
- Support large scale embedding, incremental saving
- Many parallel strategies: ParameterServer, Mirrored, MultiWorker
- Easy deployment to EAS: automatic scaling, easy monitoring
- Consistency guarantee: train and serving
- DSSM / MIND / DropoutNet / CoMetricLearningI2I
- W&D / DeepFM / MultiTower / DCN / DIN / BST
- MMoE / ESMM / DBMTL / PLE
- CMBF
- More models in development
- Easy to implement customized models
- Not need to care about data pipelines
- Run knn algorithm of vectors in distribute environment
Running Platform:
- 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.
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DingDing Group: 32260796. (EasyRec usage general discussion.)
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Email Group: easy_rec@service.aliyun.com.
- 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.