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.It is compatible with tensorflow 1.4+ and 2.0+.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, 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, ## DisscussionGroup
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