shenweichen/DeepCTR-Torch

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


repo name	shenweichen/DeepCTR-Torch
repo link	https://github.com/shenweichen/DeepCTR-Torch
homepage	https://deepctr-torch.readthedocs.io/en/latest/index.html
language	Python
size (curr.)	4342 kB
stars (curr.)	393
created	2019-09-06
license	Apache License 2.0

DeepCTR-Torch

PyTorch version of 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.You can use any complex model with model.fit()and model.predict() .Install through pip install -U deepctr-torch.

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
ONN	[arxiv 2019]Operation-aware Neural Networks for User Response Prediction
FiBiNET	[RecSys 2019]FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction