Skip to main content

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

Project description


Python Versions Downloads PyPI Version GitHub Issues

Documentation Status CI status codecov Disscussion License

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.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




Contributors(welcome to join us!)

Shen Weichen

Core Dev
Zhejiang Unversity

Wang Ze

Core Dev
Beihang University

Zhang Wutong

Core Dev
Beijing University
of Posts and

Zan Shuxun

Core Dev
Beijing University
of Posts and

Zhang Yuefeng

Core Dev
Peking University

Huo Junyi

Core Dev
University of Southampton

Zeng Kai


Chen K



Tongji University

Xu Qidi

University of
Electronic Science and
Technology of China

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for deepctr-torch, version 0.2.5
Filename, size File type Python version Upload date Hashes
Filename, size deepctr_torch-0.2.5-py3-none-any.whl (58.5 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size deepctr-torch-0.2.5.tar.gz (40.4 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page