July 28, 2019

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shenweichen/DeepCTR

shenweichen/DeepCTR

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

repo name shenweichen/DeepCTR
repo link https://github.com/shenweichen/DeepCTR
homepage https://deepctr-doc.readthedocs.io/en/latest/index.html
language Python
size (curr.) 5662 kB
stars (curr.) 2903
created 2017-10-07
license Apache License 2.0

DeepCTR

Python Versions TensorFlow Versions Downloads PyPI Version GitHub Issues

Documentation Status Build Status Coverage Status Codacy Badge Disscussion License

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
FLEN [arxiv 2019]FLEN: Leveraging Field for Scalable CTR Prediction

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