{"id":13712265,"url":"https://github.com/shenweichen/DeepCTR-Torch","last_synced_at":"2025-05-06T21:33:48.536Z","repository":{"id":37692941,"uuid":"206794657","full_name":"shenweichen/DeepCTR-Torch","owner":"shenweichen","description":"【PyTorch】Easy-to-use,Modular and Extendible package of deep-learning based CTR 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DeepCTR-Torch\n\n[![Python Versions](https://img.shields.io/pypi/pyversions/deepctr-torch.svg)](https://pypi.org/project/deepctr-torch)\n[![Downloads](https://pepy.tech/badge/deepctr-torch)](https://pepy.tech/project/deepctr-torch)\n[![PyPI Version](https://img.shields.io/pypi/v/deepctr-torch.svg)](https://pypi.org/project/deepctr-torch)\n[![GitHub Issues](https://img.shields.io/github/issues/shenweichen/deepctr-torch.svg\n)](https://github.com/shenweichen/deepctr-torch/issues)\n\n\n[![Documentation Status](https://readthedocs.org/projects/deepctr-torch/badge/?version=latest)](https://deepctr-torch.readthedocs.io/)\n![CI status](https://github.com/shenweichen/deepctr-torch/workflows/CI/badge.svg)\n[![codecov](https://codecov.io/gh/shenweichen/DeepCTR-Torch/branch/master/graph/badge.svg?token=m6v89eYOjp)](https://codecov.io/gh/shenweichen/DeepCTR-Torch)\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-torch.svg)](https://github.com/shenweichen/deepctr-torch/blob/master/LICENSE)\n\nPyTorch version of [DeepCTR](https://github.com/shenweichen/DeepCTR).\n\nDeepCTR is a **Easy-to-use**,**Modular** and **Extendible** package of deep-learning based CTR models along with lots of core components layers which can be used to build your own custom model easily.You can use any complex model with `model.fit()`and `model.predict()` .Install through `pip install -U deepctr-torch`.\n\nLet's [**Get Started!**](https://deepctr-torch.readthedocs.io/en/latest/Quick-Start.html)([Chinese Introduction](https://zhuanlan.zhihu.com/p/53231955))\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|    Deep Interest Evolution Network     | [AAAI 2019][Deep Interest Evolution Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1809.03672.pdf)                                            |\n|                AutoInt                 | [CIKM 2019][AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/abs/1810.11921)                              |\n|                  ONN                   | [arxiv 2019][Operation-aware Neural Networks for User Response Prediction](https://arxiv.org/pdf/1904.12579.pdf)                                                |\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|                  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|                  AFN                   | [AAAI 2020][Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions](https://arxiv.org/pdf/1909.03276)   |\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://dl.acm.org/doi/10.1145/3209978.3210104)                       |\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\n\n\n## DisscussionGroup \u0026 Related Projects\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- Related Projects\n\n  - [AlgoNotes](https://github.com/shenweichen/AlgoNotes)\n  - [DeepCTR](https://github.com/shenweichen/DeepCTR)\n  - [DeepMatch](https://github.com/shenweichen/DeepMatch)\n  - [GraphEmbedding](https://github.com/shenweichen/GraphEmbedding)\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 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\u003e Alibaba Group \u003c/p\u003e​\n      \u003c/td\u003e\n      \u003ctd\u003e\n         \u003ca href=\"https://github.com/weberrr\"\u003e\u003cimg width=\"70\" height=\"70\" src=\"https://github.com/weberrr.png?s=40\" alt=\"pic\"\u003e\u003c/a\u003e\u003cbr\u003e\n         \u003ca href=\"https://github.com/weberrr\"\u003eWang Ze\u003c/a\u003e ​\n        \u003cp\u003e Meituan \u003c/p\u003e​\n      \u003c/td\u003e\n      \u003ctd\u003e\n        ​ \u003ca href=\"https://github.com/wutongzhang\"\u003e\u003cimg width=\"70\" height=\"70\" src=\"https://github.com/wutongzhang.png?s=40\" alt=\"pic\"\u003e\u003c/a\u003e\u003cbr\u003e\n         \u003ca href=\"https://github.com/wutongzhang\"\u003eZhang Wutong\u003c/a\u003e\n         \u003cp\u003e Tencent \u003c/p\u003e​\n      \u003c/td\u003e\n      \u003ctd\u003e\n        ​ \u003ca href=\"https://github.com/ZhangYuef\"\u003e\u003cimg width=\"70\" height=\"70\" src=\"https://github.com/ZhangYuef.png?s=40\" alt=\"pic\"\u003e\u003c/a\u003e\u003cbr\u003e\n        ​ \u003ca href=\"https://github.com/ZhangYuef\"\u003eZhang Yuefeng\u003c/a\u003e\n        \u003cp\u003e Peking University  \u003c/p\u003e​\n      \u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr align=\"center\"\u003e\n      \u003ctd\u003e\n        ​ \u003ca href=\"https://github.com/JyiHUO\"\u003e\u003cimg width=\"70\" height=\"70\" src=\"https://github.com/JyiHUO.png?s=40\" alt=\"pic\"\u003e\u003c/a\u003e\u003cbr\u003e\n        ​ \u003ca href=\"https://github.com/JyiHUO\"\u003eHuo Junyi\u003c/a\u003e\n        \u003cp\u003e\n        University of Southampton \u003cbr\u003e \u003cbr\u003e  \u003c/p\u003e​\n      \u003c/td\u003e\n      \u003ctd\u003e\n        ​ \u003ca href=\"https://github.com/Zengai\"\u003e\u003cimg width=\"70\" height=\"70\" src=\"https://github.com/Zengai.png?s=40\" alt=\"pic\"\u003e\u003c/a\u003e\u003cbr\u003e\n        ​ \u003ca href=\"https://github.com/Zengai\"\u003eZeng Kai\u003c/a\u003e ​\n        \u003cp\u003e\n        SenseTime \u003cbr\u003e \u003cbr\u003e  \u003c/p\u003e​\n      \u003c/td\u003e\n      \u003ctd\u003e\n        ​ \u003ca href=\"https://github.com/chenkkkk\"\u003e\u003cimg width=\"70\" height=\"70\" src=\"https://github.com/chenkkkk.png?s=40\" alt=\"pic\"\u003e\u003c/a\u003e\u003cbr\u003e\n        ​ \u003ca href=\"https://github.com/chenkkkk\"\u003eChen K\u003c/a\u003e ​\n        \u003cp\u003e\n        NetEase \u003cbr\u003e  \u003cbr\u003e  \u003c/p\u003e​\n      \u003c/td\u003e\n      \u003ctd\u003e\n        ​ \u003ca href=\"https://github.com/WeiyuCheng\"\u003e\u003cimg width=\"70\" height=\"70\" src=\"https://github.com/WeiyuCheng.png?s=40\" alt=\"pic\"\u003e\u003c/a\u003e\u003cbr\u003e\n        ​ \u003ca href=\"https://github.com/WeiyuCheng\"\u003eCheng Weiyu\u003c/a\u003e ​\n        \u003cp\u003e\n        Shanghai Jiao Tong University\u003c/p\u003e​\n      \u003c/td\u003e\n      \u003ctd\u003e\n        ​ \u003ca href=\"https://github.com/tangaqi\"\u003e\u003cimg width=\"70\" height=\"70\" src=\"https://github.com/tangaqi.png?s=40\" alt=\"pic\"\u003e\u003c/a\u003e\u003cbr\u003e\n        ​ \u003ca href=\"https://github.com/tangaqi\"\u003eTang\u003c/a\u003e\n        \u003cp\u003e\n        Tongji University \u003cbr\u003e \u003cbr\u003e  \u003c/p\u003e​\n      \u003c/td\u003e\n    \u003c/tr\u003e\n  \u003c/tbody\u003e\n\u003c/table\u003e","funding_links":[],"categories":["Recommendation, Advertisement \u0026 Ranking","Python","Recommender Systems"],"sub_categories":["Others"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshenweichen%2FDeepCTR-Torch","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshenweichen%2FDeepCTR-Torch","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshenweichen%2FDeepCTR-Torch/lists"}