🎉 See our ongoing recommendation framework TorchEasyRec ! 🎉 This evolution of EasyRec is built on PyTorch, featuring GPU acceleration and hybrid parallelism for enhanced performance.

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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 examples
• MaxCompute
• EMR-DataScience
• PAI-DSW (DEMO)

• 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. click this url or scan QrCode to joindinggroup1.png
• DingDing Group2: 37930014162, click this url or scan QrCode to joindinggroup2.png
• 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.