# DeepCTR-Torch **Repository Path**: zanshuxun/DeepCTR-Torch ## Basic Information - **Project Name**: DeepCTR-Torch - **Description**: No description available - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2020-11-27 - **Last Updated**: 2021-05-30 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # DeepCTR-Torch [![Python Versions](https://img.shields.io/pypi/pyversions/deepctr-torch.svg)](https://pypi.org/project/deepctr-torch) [![Downloads](https://pepy.tech/badge/deepctr-torch)](https://pepy.tech/project/deepctr-torch) [![PyPI Version](https://img.shields.io/pypi/v/deepctr-torch.svg)](https://pypi.org/project/deepctr-torch) [![GitHub Issues](https://img.shields.io/github/issues/shenweichen/deepctr-torch.svg )](https://github.com/shenweichen/deepctr-torch/issues) [![Documentation Status](https://readthedocs.org/projects/deepctr-torch/badge/?version=latest)](https://deepctr-torch.readthedocs.io/) ![CI status](https://github.com/shenweichen/deepctr-torch/workflows/CI/badge.svg) [![codecov](https://codecov.io/gh/shenweichen/DeepCTR-Torch/branch/master/graph/badge.svg)](https://codecov.io/gh/shenweichen/DeepCTR-Torch) [![Disscussion](https://img.shields.io/badge/chat-wechat-brightgreen?style=flat)](./README.md#disscussiongroup) [![License](https://img.shields.io/github/license/shenweichen/deepctr-torch.svg)](https://github.com/shenweichen/deepctr-torch/blob/master/LICENSE) PyTorch version of [DeepCTR](https://github.com/shenweichen/DeepCTR). DeepCTR 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`. Let's [**Get Started!**](https://deepctr-torch.readthedocs.io/en/latest/Quick-Start.html)([Chinese Introduction](https://zhuanlan.zhihu.com/p/53231955)) ## Models List | Model | Paper | | :------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------- | | 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) | | 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) | | Product-based Neural Network | [ICDM 2016][Product-based neural networks for user response prediction](https://arxiv.org/pdf/1611.00144.pdf) | | Wide & Deep | [DLRS 2016][Wide & Deep Learning for Recommender Systems](https://arxiv.org/pdf/1606.07792.pdf) | | DeepFM | [IJCAI 2017][DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](http://www.ijcai.org/proceedings/2017/0239.pdf) | | 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) | | Deep & Cross Network | [ADKDD 2017][Deep & Cross Network for Ad Click Predictions](https://arxiv.org/abs/1708.05123) | | Attentional Factorization Machine | [IJCAI 2017][Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](http://www.ijcai.org/proceedings/2017/435) | | Neural Factorization Machine | [SIGIR 2017][Neural Factorization Machines for Sparse Predictive Analytics](https://arxiv.org/pdf/1708.05027.pdf) | | xDeepFM | [KDD 2018][xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://arxiv.org/pdf/1803.05170.pdf) | | Deep Interest Network | [KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1706.06978.pdf) | | Deep Interest Evolution Network | [AAAI 2019][Deep Interest Evolution Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1809.03672.pdf) | | AutoInt | [CIKM 2019][AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/abs/1810.11921) | | ONN | [arxiv 2019][Operation-aware Neural Networks for User Response Prediction](https://arxiv.org/pdf/1904.12579.pdf) | | FiBiNET | [RecSys 2019][FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction](https://arxiv.org/pdf/1905.09433.pdf) | | IFM | [IJCAI 2019][An Input-aware Factorization Machine for Sparse Prediction](https://www.ijcai.org/Proceedings/2019/0203.pdf) | | DCN V2 | [arxiv 2020][DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://arxiv.org/abs/2008.13535) | | DIFM | [IJCAI 2020][A Dual Input-aware Factorization Machine for CTR Prediction](https://www.ijcai.org/Proceedings/2020/0434.pdf) | ## DisscussionGroup & Related Projects
公众号：浅梦的学习笔记

 微信：deepctrbot
## Contributors([welcome to join us!](./CONTRIBUTING.md))
​ pic
​ Shen Weichen ​

Core Dev
Zhejiang Unversity

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​ Zan Shuxun

Core Dev
Beijing University
of Posts and
Telecommunications

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Wang Ze ​

Core Dev
Beihang University

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Zhang Wutong

Core Dev
Beijing University
of Posts and
Telecommunications

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​ Zhang Yuefeng

Core Dev
Peking University

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​ pic
​ Huo Junyi

Core Dev
University of Southampton

​ 		​ pic
​ Zeng Kai ​

Dev
SenseTime

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​ Chen K ​

Dev
NetEase

​ 		​ pic
​ Tang

Test
Tongji University

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​ Xu Qidi ​

Dev
University of
Electronic Science and
Technology of China

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