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 easily build custom models.You can use any complex model with model.fit() ，and model.predict() .

Provide tf.keras.Model like interface for quick experiment . example
Provide tensorflow estimator interface for large scale data and distributed training . example
It is compatible with both tf 1.x and tf 2.x.

Some related projects:

DeepMatch: https://github.com/shenweichen/DeepMatch
DeepCTR-Torch: https://github.com/shenweichen/DeepCTR-Torch

Let's Get Started!(Chinese Introduction) and welcome to join us!

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
AutoInt	[CIKM 2019]AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
Deep Interest Evolution Network	[AAAI 2019]Deep Interest Evolution Network for Click-Through Rate Prediction
FwFM	[WWW 2018]Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising
ONN	[arxiv 2019]Operation-aware Neural Networks for User Response Prediction
FGCNN	[WWW 2019]Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction
Deep Session Interest Network	[IJCAI 2019]Deep Session Interest Network for Click-Through Rate Prediction
FiBiNET	[RecSys 2019]FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
FLEN	[arxiv 2019]FLEN: Leveraging Field for Scalable CTR Prediction
BST	[DLP-KDD 2019]Behavior sequence transformer for e-commerce recommendation in Alibaba
IFM	[IJCAI 2019]An Input-aware Factorization Machine for Sparse Prediction
DCN V2	[arxiv 2020]DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
DIFM	[IJCAI 2020]A Dual Input-aware Factorization Machine for CTR Prediction
FEFM and DeepFEFM	[arxiv 2020]Field-Embedded Factorization Machines for Click-through rate prediction
SharedBottom	[arxiv 2017]An Overview of Multi-Task Learning in Deep Neural Networks
ESMM	[SIGIR 2018]Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate
MMOE	[KDD 2018]Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts
PLE	[RecSys 2020]Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations

Citation

Weichen Shen. (2017). DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR models. https://github.com/shenweichen/deepctr.

If you find this code useful in your research, please cite it using the following BibTeX:

@misc{shen2017deepctr,
  author = {Weichen Shen},
  title = {DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR models},
  year = {2017},
  publisher = {GitHub},
  journal = {GitHub Repository},
  howpublished = {\url{https://github.com/shenweichen/deepctr}},
}