Easy-to-use,Modular and Extendible package of deep learning based CTR(Click Through Rate) prediction models with tensorflow 1.x and 2.x .
Project description
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.You can use any complex model with model.fit()
,and model.predict()
.
- Provide
tf.keras.Model
like interfaces for quick experiment. example - Provide
tensorflow estimator
interface for large scale data and distributed training. example - It is compatible with both
tf 1.x
andtf 2.x
.
Some related projects:
- DeepMatch: https://github.com/shenweichen/DeepMatch
- DeepCTR-Torch: https://github.com/shenweichen/DeepCTR-Torch
Let's Get Started!(Chinese Introduction) and welcome to join us!
Models List
Citation
- Weichen Shen. (2017). DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR models. https://github.com/shenweichen/deepctr.
If you find this code useful in your research, please cite it using the following BibTeX:
@misc{shen2017deepctr,
author = {Weichen Shen},
title = {DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR models},
year = {2017},
publisher = {GitHub},
journal = {GitHub Repository},
howpublished = {\url{https://github.com/shenweichen/deepctr}},
}
DisscussionGroup
- Github Discussions
- Wechat Discussions
公众号:浅梦学习笔记 | 微信:deepctrbot | 学习小组 加入 主题集合 |
---|---|---|
Main contributors(welcome to join us!)
Shen Weichen Alibaba Group |
Zan Shuxun Alibaba Group |
Harshit Pande Amazon |
Lai Mincai ByteDance |
Li Zichao ByteDance |
Tan Tingyi Chongqing University |
Project details
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