README

<p align="center"> <a href="https://pypi.org/project/torchctr/"> <img alt="PyPI" src="https://img.shields.io/pypi/v/torchctr.svg"> </a> <a href='https://github.com/guokr/torchctr/blob/master/LICENSE'> <img alt="PyPI - License" src="https://img.shields.io/pypi/l/gnes.svg"> </a> </p> <p align="center"> <a href="#install">Install</a> | <a href="#getting-started">Getting Started</a> | <a href="#documentation">Documentation</a> | </p> <h2 align="center">TorchCTR</h2>

TorchCTR is a scalable and easy-to-use ML package for CTR (Click Through Rate) prediction and ranking in recommender system with Facebook PyTorch.

Classic Models

Deep Learning Models

[x] Wide and Deep by Google
[x] DeepFM by Huawei & HIT
[x] Neural Factorization Machine by NUS
[x] Field-aware Neural Factorization Machine
[ ] Factorization-supported Neural Network
[ ] Product-based Neural Network
[ ] Attentional Factorization Machine
[ ] Deep and Cross Network
[ ] Deep Interest Network
[ ] eXtreme Deep Factorization Machine

<h2 align="center">Install</h2>

pip install torchctr --user

<h2 align="center">Getting Started</h2>

please check exmaples/

<h2 align="center">Documentation</h2>

Official Documentation

TorchCTR

Install / Use

README

Classic Models

Deep Learning Models

Related Skills