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.It is implemented by tensorflow.You can use any complex model with model.fit()and model.predict() .

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
AutoInt	[arxiv 2018]AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
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
NFFM	[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