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Easy-to-use,Modular and Extendible package of deep learning based CTR(Click Through Rate) prediction models with PyTorch

Project description

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

Model Paper
Convolutional Click Prediction Model [CIKM 2015]A Convolutional Click Prediction Model
Factorization-supported Neural Network [ECIR 2016]Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction
Product-based Neural Network [ICDM 2016]Product-based neural networks for user response prediction
Wide & Deep [DLRS 2016]Wide & Deep Learning for Recommender Systems
DeepFM [IJCAI 2017]DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
Piece-wise Linear Model [arxiv 2017]Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction
Deep & Cross Network [ADKDD 2017]Deep & Cross Network for Ad Click Predictions
Attentional Factorization Machine [IJCAI 2017]Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks
Neural Factorization Machine [SIGIR 2017]Neural Factorization Machines for Sparse Predictive Analytics
xDeepFM [KDD 2018]xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
Deep Interest Network [KDD 2018]Deep Interest Network for Click-Through Rate Prediction
Deep Interest Evolution Network [AAAI 2019]Deep Interest Evolution Network for Click-Through Rate Prediction
AutoInt [CIKM 2019]AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
ONN [arxiv 2019]Operation-aware Neural Networks for User Response Prediction
FiBiNET [RecSys 2019]FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
DCN V2 [arxiv 2020]DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems

DisscussionGroup & Related Projects

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公众号:浅梦的学习笔记

微信:deepctrbot

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Contributors(welcome to join us!)

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Shen Weichen

Core Dev
Zhejiang Unversity

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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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Zan Shuxun

Core Dev
Beijing University
of Posts and
Telecommunications

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Zhang Yuefeng

Core Dev
Peking University

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Huo Junyi

Core Dev
University of Southampton

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Zeng Kai

Dev
SenseTime

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Chen K

Dev
NetEase

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Tang

Test
Tongji University

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Xu Qidi

Dev
University of
Electronic Science and
Technology of China

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