DeepCTR

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

5806

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Python

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 interfaces 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
EDCN	[KDD 2021]Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models

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}},
}