{"id":15039409,"url":"https://github.com/shenweichen/deepctr","last_synced_at":"2025-05-12T15:16:21.262Z","repository":{"id":37390759,"uuid":"106080065","full_name":"shenweichen/DeepCTR","owner":"shenweichen","description":"Easy-to-use,Modular and Extendible package of deep-learning based CTR models 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DeepCTR\n\n[![Python Versions](https://img.shields.io/pypi/pyversions/deepctr.svg)](https://pypi.org/project/deepctr)\n[![TensorFlow Versions](https://img.shields.io/badge/TensorFlow-1.4+/2.0+-blue.svg)](https://pypi.org/project/deepctr)\n[![Downloads](https://pepy.tech/badge/deepctr)](https://pepy.tech/project/deepctr)\n[![PyPI Version](https://img.shields.io/pypi/v/deepctr.svg)](https://pypi.org/project/deepctr)\n[![GitHub Issues](https://img.shields.io/github/issues/shenweichen/deepctr.svg\n)](https://github.com/shenweichen/deepctr/issues)\n\u003c!-- [![Activity](https://img.shields.io/github/last-commit/shenweichen/deepctr.svg)](https://github.com/shenweichen/DeepCTR/commits/master) --\u003e\n\n\n[![Documentation Status](https://readthedocs.org/projects/deepctr-doc/badge/?version=latest)](https://deepctr-doc.readthedocs.io/)\n![CI status](https://github.com/shenweichen/deepctr/workflows/CI/badge.svg)\n[![codecov](https://codecov.io/gh/shenweichen/DeepCTR/branch/master/graph/badge.svg)](https://codecov.io/gh/shenweichen/DeepCTR)\n[![Codacy Badge](https://api.codacy.com/project/badge/Grade/d4099734dc0e4bab91d332ead8c0bdd0)](https://www.codacy.com/gh/shenweichen/DeepCTR?utm_source=github.com\u0026amp;utm_medium=referral\u0026amp;utm_content=shenweichen/DeepCTR\u0026amp;utm_campaign=Badge_Grade)\n[![Disscussion](https://img.shields.io/badge/chat-wechat-brightgreen?style=flat)](./README.md#DisscussionGroup)\n[![License](https://img.shields.io/github/license/shenweichen/deepctr.svg)](https://github.com/shenweichen/deepctr/blob/master/LICENSE)\n\u003c!-- [![Gitter](https://badges.gitter.im/DeepCTR/community.svg)](https://gitter.im/DeepCTR/community?utm_source=badge\u0026utm_medium=badge\u0026utm_campaign=pr-badge) --\u003e\n\n\nDeepCTR is a **Easy-to-use**, **Modular** and **Extendible** package of deep-learning based CTR models along with lots of\ncore components layers which can be used to easily build custom models.You can use any complex model with `model.fit()`\n，and `model.predict()` .\n\n- Provide `tf.keras.Model` like interfaces for **quick experiment**. [example](https://deepctr-doc.readthedocs.io/en/latest/Quick-Start.html#getting-started-4-steps-to-deepctr)\n- Provide  `tensorflow estimator` interface for **large scale data** and **distributed training**. [example](https://deepctr-doc.readthedocs.io/en/latest/Quick-Start.html#getting-started-4-steps-to-deepctr-estimator-with-tfrecord)\n- It is compatible with both `tf 1.x`  and `tf 2.x`.\n\nSome related projects:\n\n- DeepMatch: https://github.com/shenweichen/DeepMatch\n- DeepCTR-Torch: https://github.com/shenweichen/DeepCTR-Torch\n\nLet's [**Get Started!**](https://deepctr-doc.readthedocs.io/en/latest/Quick-Start.html)([Chinese\nIntroduction](https://zhuanlan.zhihu.com/p/53231955)) and [welcome to join us!](./CONTRIBUTING.md)\n\n## Models List\n\n|                 Model                  | Paper                                                                                                                                                           |\n| :------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n|  Convolutional Click Prediction Model  | [CIKM 2015][A Convolutional Click Prediction Model](http://ir.ia.ac.cn/bitstream/173211/12337/1/A%20Convolutional%20Click%20Prediction%20Model.pdf)             |\n| Factorization-supported Neural Network | [ECIR 2016][Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction](https://arxiv.org/pdf/1601.02376.pdf)                    |\n|      Product-based Neural Network      | [ICDM 2016][Product-based neural networks for user response prediction](https://arxiv.org/pdf/1611.00144.pdf)                                                   |\n|              Wide \u0026 Deep               | [DLRS 2016][Wide \u0026 Deep Learning for Recommender Systems](https://arxiv.org/pdf/1606.07792.pdf)                                                                 |\n|                 DeepFM                 | [IJCAI 2017][DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](http://www.ijcai.org/proceedings/2017/0239.pdf)                           |\n|        Piece-wise Linear Model         | [arxiv 2017][Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction](https://arxiv.org/abs/1704.05194)                                 |\n|          Deep \u0026 Cross Network          | [ADKDD 2017][Deep \u0026 Cross Network for Ad Click Predictions](https://arxiv.org/abs/1708.05123)                                                                   |\n|   Attentional Factorization Machine    | [IJCAI 2017][Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](http://www.ijcai.org/proceedings/2017/435) |\n|      Neural Factorization Machine      | [SIGIR 2017][Neural Factorization Machines for Sparse Predictive Analytics](https://arxiv.org/pdf/1708.05027.pdf)                                               |\n|                xDeepFM                 | [KDD 2018][xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://arxiv.org/pdf/1803.05170.pdf)                         |\n|         Deep Interest Network          | [KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1706.06978.pdf)     |\n|                AutoInt                 | [CIKM 2019][AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/abs/1810.11921)                              |\n|    Deep Interest Evolution Network     | [AAAI 2019][Deep Interest Evolution Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1809.03672.pdf)                                            |\n|                FwFM                    | [WWW 2018][Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising](https://arxiv.org/pdf/1806.03514.pdf)                |\n|                  ONN                  | [arxiv 2019][Operation-aware Neural Networks for User Response Prediction](https://arxiv.org/pdf/1904.12579.pdf)                                                |\n|                 FGCNN                  | [WWW 2019][Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction ](https://arxiv.org/pdf/1904.04447)                             |\n|     Deep Session Interest Network      | [IJCAI 2019][Deep Session Interest Network for Click-Through Rate Prediction ](https://arxiv.org/abs/1905.06482)                                                |\n|                FiBiNET                 | [RecSys 2019][FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction](https://arxiv.org/pdf/1905.09433.pdf)   |\n|                FLEN                    | [arxiv 2019][FLEN: Leveraging Field for Scalable CTR Prediction](https://arxiv.org/pdf/1911.04690.pdf)   |\n|                 BST                   | [DLP-KDD 2019][Behavior sequence transformer for e-commerce recommendation in Alibaba](https://arxiv.org/pdf/1905.06874.pdf)                           | \n|                IFM                 | [IJCAI 2019][An Input-aware Factorization Machine for Sparse Prediction](https://www.ijcai.org/Proceedings/2019/0203.pdf)   |\n|                DCN V2                    | [arxiv 2020][DCN V2: Improved Deep \u0026 Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://arxiv.org/abs/2008.13535)   |\n|                DIFM                 | [IJCAI 2020][A Dual Input-aware Factorization Machine for CTR Prediction](https://www.ijcai.org/Proceedings/2020/0434.pdf)   |\n|   FEFM and DeepFEFM                    | [arxiv 2020][Field-Embedded Factorization Machines for Click-through rate prediction](https://arxiv.org/abs/2009.09931)                                         |\n|              SharedBottom               | [arxiv 2017][An Overview of Multi-Task Learning in Deep Neural Networks](https://arxiv.org/pdf/1706.05098.pdf)  |\n|   ESMM                    | [SIGIR 2018][Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://arxiv.org/abs/1804.07931)                       |\n|   MMOE                    | [KDD 2018][Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts](https://dl.acm.org/doi/abs/10.1145/3219819.3220007)                   |\n|   PLE                    | [RecSys 2020][Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations](https://dl.acm.org/doi/10.1145/3383313.3412236)                   |\n|   EDCN                   | [KDD 2021][Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models](https://dlp-kdd.github.io/assets/pdf/DLP-KDD_2021_paper_12.pdf)                   |\n\n## Citation\n\n- Weichen Shen. (2017). DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR\n  models. https://github.com/shenweichen/deepctr.\n\nIf you find this code useful in your research, please cite it using the following BibTeX:\n\n```bibtex\n@misc{shen2017deepctr,\n  author = {Weichen Shen},\n  title = {DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR models},\n  year = {2017},\n  publisher = {GitHub},\n  journal = {GitHub Repository},\n  howpublished = {\\url{https://github.com/shenweichen/deepctr}},\n}\n```\n\n## DisscussionGroup\n\n- [Github Discussions](https://github.com/shenweichen/DeepCTR/discussions)\n- Wechat Discussions\n\n|公众号：浅梦学习笔记|微信：deepctrbot|学习小组 [加入](https://t.zsxq.com/026UJEuzv) [主题集合](https://mp.weixin.qq.com/mp/appmsgalbum?__biz=MjM5MzY4NzE3MA==\u0026action=getalbum\u0026album_id=1361647041096843265\u0026scene=126#wechat_redirect)|\n|:--:|:--:|:--:|\n| [![公众号](./docs/pics/code.png)](https://github.com/shenweichen/AlgoNotes)| [![微信](./docs/pics/deepctrbot.png)](https://github.com/shenweichen/AlgoNotes)|[![学习小组](./docs/pics/planet_github.png)](https://t.zsxq.com/026UJEuzv)|\n\n## Main contributors([welcome to join us!](./CONTRIBUTING.md))\n\n\u003ctable border=\"0\"\u003e\n  \u003ctbody\u003e\n    \u003ctr align=\"center\" \u003e\n      \u003ctd\u003e\n        ​ \u003ca href=\"https://github.com/shenweichen\"\u003e\u003cimg width=\"70\" height=\"70\" src=\"https://github.com/shenweichen.png?s=40\" alt=\"pic\"\u003e\u003c/a\u003e\u003cbr\u003e\n        ​ \u003ca href=\"https://github.com/shenweichen\"\u003eShen Weichen\u003c/a\u003e ​\n        \u003cp\u003e\n        Alibaba Group  \u003c/p\u003e​\n      \u003c/td\u003e\n      \u003ctd\u003e\n         \u003ca href=\"https://github.com/zanshuxun\"\u003e\u003cimg width=\"70\" height=\"70\" src=\"https://github.com/zanshuxun.png?s=40\" alt=\"pic\"\u003e\u003c/a\u003e\u003cbr\u003e\n         \u003ca href=\"https://github.com/zanshuxun\"\u003eZan Shuxun\u003c/a\u003e ​\n        \u003cp\u003eAlibaba Group  \u003c/p\u003e​\n      \u003c/td\u003e\n      \u003ctd\u003e\n        ​ \u003ca href=\"https://github.com/pandeconscious\"\u003e\u003cimg width=\"70\" height=\"70\" src=\"https://github.com/pandeconscious.png?s=40\" alt=\"pic\"\u003e\u003c/a\u003e\u003cbr\u003e\n        ​ \u003ca href=\"https://github.com/pandeconscious\"\u003eHarshit Pande\u003c/a\u003e\n        \u003cp\u003e Amazon   \u003c/p\u003e​\n      \u003c/td\u003e\n      \u003ctd\u003e\n        ​ \u003ca href=\"https://github.com/morningsky\"\u003e\u003cimg width=\"70\" height=\"70\" src=\"https://github.com/morningsky.png?s=40\" alt=\"pic\"\u003e\u003c/a\u003e\u003cbr\u003e\n        ​ \u003ca href=\"https://github.com/morningsky\"\u003eLai Mincai\u003c/a\u003e\n        \u003cp\u003e ByteDance \u003c/p\u003e​\n      \u003c/td\u003e\n      \u003ctd\u003e\n        ​ \u003ca href=\"https://github.com/codewithzichao\"\u003e\u003cimg width=\"70\" height=\"70\" src=\"https://github.com/codewithzichao.png?s=40\" alt=\"pic\"\u003e\u003c/a\u003e\u003cbr\u003e\n        ​ \u003ca href=\"https://github.com/codewithzichao\"\u003eLi Zichao\u003c/a\u003e\n        \u003cp\u003e ByteDance   \u003c/p\u003e​\n      \u003c/td\u003e\n      \u003ctd\u003e\n        ​ \u003ca href=\"https://github.com/TanTingyi\"\u003e\u003cimg width=\"70\" height=\"70\" src=\"https://github.com/TanTingyi.png?s=40\" alt=\"pic\"\u003e\u003c/a\u003e\u003cbr\u003e\n         \u003ca href=\"https://github.com/TanTingyi\"\u003eTan Tingyi\u003c/a\u003e\n         \u003cp\u003e  Chongqing University \u003cbr\u003e of  Posts and \u003cbr\u003e Telecommunications   \u003c/p\u003e​\n      \u003c/td\u003e\n    \u003c/tr\u003e\n  \u003c/tbody\u003e\n\u003c/table\u003e\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshenweichen%2Fdeepctr","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshenweichen%2Fdeepctr","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshenweichen%2Fdeepctr/lists"}