https://github.com/guokr/torchctr
CTR Prediction on PyTorch
https://github.com/guokr/torchctr
classfication ctr ctr-prediction deep-learning factorization-machine pytorch
Last synced: about 1 month ago
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CTR Prediction on PyTorch
- Host: GitHub
- URL: https://github.com/guokr/torchctr
- Owner: guokr
- Created: 2019-08-05T08:45:43.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2019-09-02T07:45:02.000Z (over 5 years ago)
- Last Synced: 2025-03-24T09:05:04.044Z (about 2 months ago)
- Topics: classfication, ctr, ctr-prediction, deep-learning, factorization-machine, pytorch
- Language: Python
- Homepage: https://guokr.github.io/TorchCTR/
- Size: 5.65 MB
- Stars: 14
- Watchers: 4
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
Install |
Getting Started |
Documentation |
TorchCTR
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
- [x] [Logistic Regression](https://en.wikipedia.org/wiki/Logistic_regression)
- [x] [Factorization Machine by Osaka Univ.](https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf)
- [x] [Field-aware Factorization Machine by Criteo, CMU & NTU](https://www.csie.ntu.edu.tw/~cjlin/papers/ffm.pdf)
### Deep Learning Models
- [x] [Wide and Deep by Google](https://arxiv.org/abs/1606.07792)
- [x] [DeepFM by Huawei & HIT](https://arxiv.org/abs/1703.04247)
- [x] [Neural Factorization Machine by NUS](https://arxiv.org/pdf/1708.05027.pdf)
- [x] [Field-aware Neural Factorization Machine](https://arxiv.org/abs/1902.09096)
- [ ] [Factorization-supported Neural Network](https://arxiv.org/abs/1601.02376)
- [ ] [Product-based Neural Network](https://arxiv.org/abs/1611.00144)
- [ ] [Attentional Factorization Machine](https://arxiv.org/abs/1708.04617)
- [ ] [Deep and Cross Network](https://arxiv.org/abs/1708.05123)
- [ ] [Deep Interest Network](https://arxiv.org/abs/1706.06978)
- [ ] [eXtreme Deep Factorization Machine](https://arxiv.org/abs/1803.05170)
Install
`pip install torchctr --user`
Getting Started
please check exmaples/
Documentation
[Official Documentation](https://guokr.github.io/TorchCTR/)