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deepctr-torch
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deepctr-torch

【PyTorch】Easy-to-use,Modular and Extendible package of deep-learning based CTR models.

by 浅梦

0.2.8 (see all)License:Apache-2.0
pypi i deepctr-torch
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DeepCTR-Torch

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PyTorch version of 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!(Chinese Introduction)

Models List

ModelPaper
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)
AFN[AAAI 2020][Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions](https://arxiv.org/pdf/1909.03276)
公众号:浅梦学习笔记微信:deepctrbot学习小组 加入 主题集合
公众号微信学习小组

Main Contributors(welcome to join us!)

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Shen Weichen

Alibaba Group

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Zan Shuxun

Alibaba Group

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Wang Ze

Meituan

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Zhang Wutong

Tencent

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Zhang Yuefeng

Peking University

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Huo Junyi

University of Southampton

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Zeng Kai

SenseTime

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Chen K

NetEase

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Cheng Weiyu

Shanghai Jiao Tong University

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Tang

Tongji University

GitHub Stars

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LAST COMMIT

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17

OPEN ISSUES

28

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2yrs ago
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