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https://github.com/alibaba/easyrec

A framework for large scale recommendation algorithms.
https://github.com/alibaba/easyrec

autoint automl capsule-network ctr-prediction deepfm deepmatching din dlrm dssm eges esmm mind multi-task-learning online-learning pdn recommendation-algorithms recommender-system transformers-models wide-and-deep

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A framework for large scale recommendation algorithms.

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README 

        
# EasyRec Introduction



 



## What is EasyRec?



![intro.png](docs/images/intro.png)



### 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:



- [Local examples](examples/readme.md)

- [MaxCompute](docs/source/quick_start/mc_tutorial.md)

- [EMR-DataScience](docs/source/quick_start/emr_tutorial.md)

- [PAI-DSW (DEMO)](https://dsw-dev.data.aliyun.com/#/?fileUrl=http://easyrec.oss-cn-beijing.aliyuncs.com/dsw/easy_rec_demo.ipynb&fileName=EasyRec_DeepFM.ipynb)



 



## Why EasyRec?



### Run everywhere



- Local / [MaxCompute](https://help.aliyun.com/product/27797.html) / [EMR-DataScience](https://help.aliyun.com/document_detail/170836.html) / [DLC](https://www.alibabacloud.com/help/zh/doc-detail/165137.htm)

- TF1.12-1.15 / TF2.x / PAI-TF



### Diversified input data



- [MaxCompute Table](https://help.aliyun.com/document_detail/27819.html)

- HDFS files / Hive Table

- [OSS files](https://help.aliyun.com/product/31815.html)

- CSV files / Parquet files

- Datahub / Kafka Streams



### Simple to config



- Flexible feature config and simple model config

- [Build models by combining some components](docs/source/component/backbone.md)

- Efficient and robust feature generation\[used in taobao\]

- Nice web interface in development



### It is smart



- EarlyStop / Best Checkpoint Saver

- [Hyper Parameter Search](docs/source/automl/pai_nni_hpo.md) / [AutoFeatureCross](docs/source/automl/auto_cross_emr.md) / [Knowledge Distillation](docs/source/kd.md) / [Features Selection](docs/source/feature/feature.rst#id4)

- In development: NAS



### Large scale and easy deployment



- Support large scale embedding and [online learning](docs/source/online_train.md)

- Many parallel strategies: ParameterServer, Mirrored, MultiWorker

- Easy deployment to [EAS](https://help.aliyun.com/document_detail/113696.html): automatic scaling, easy monitoring

- Consistency guarantee: train and serving



### A variety of models



- [DSSM](docs/source/models/dssm.md) / [MIND](docs/source/models/mind.md) / [DropoutNet](docs/source/models/dropoutnet.md) / [CoMetricLearningI2I](docs/source/models/co_metric_learning_i2i.md) / [PDN](docs/source/models/pdn.md)

- [W&D](docs/source/models/wide_and_deep.md) / [DeepFM](docs/source/models/deepfm.md) / [MultiTower](docs/source/models/multi_tower.md) / [DCN](docs/source/models/dcn.md) / [FiBiNet](docs/source/models/fibinet.md) / [MaskNet](docs/source/models/masknet.md) / [PPNet](docs/source/models/ppnet.md) / [CDN](docs/source/models/cdn.md)

- [DIN](docs/source/models/din.md) / [BST](docs/source/models/bst.md) / [CL4SRec](docs/source/models/cl4srec.md)

- [MMoE](docs/source/models/mmoe.md) / [ESMM](docs/source/models/esmm.md) / [DBMTL](docs/source/models/dbmtl.md) / [AITM](docs/source/models/aitm.md) / [PLE](docs/source/models/ple.md)

- [HighwayNetwork](docs/source/models/highway.md) / [CMBF](docs/source/models/cmbf.md) / [UNITER](docs/source/models/uniter.md)

- More models in development



### Easy to customize



- Support [component-based development](docs/source/component/backbone.md)

- Easy to implement [customized models](docs/source/models/user_define.md) and [components](docs/source/component/backbone.md#id12)

- Not need to care about data pipelines



### Fast vector retrieve



- Run [knn algorithm](docs/source/vector_retrieve.md) of vectors in distribute environment



 



## Document



- [Home](https://easyrec.readthedocs.io/en/latest/)

- [FAQ](https://easyrec.readthedocs.io/en/latest/faq.html)

- [EasyRec Framework](https://easyrec.oss-cn-beijing.aliyuncs.com/docs/EasyRec.pptx)(PPT)



 



## 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](docs/source/develop.md) 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.)

- DingDing Group2: 37930014162, click [this url](https://qr.dingtalk.com/action/joingroup?code=v1,k1,oHNqtNObbu+xUClHh77gCuKdGGH8AYoQ8AjKU23zTg4=&_dt_no_comment=1&origin=11) or scan QrCode to join![new_group.jpg](docs/images/qrcode/new_group.jpg)

- 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.



 



## License



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