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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 ,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:

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


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

  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{}},


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Main contributors(welcome to join us!)

Shen Weichen

Alibaba Group

Zan Shuxun

Alibaba Group

Harshit Pande


Lai Mincai


Li Zichao


Tan Tingyi

Chongqing University
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