Metadata-Version: 2.1
Name: deepctr-torch
Version: 0.1.1
Summary: Easy-to-use,Modular and Extendible package of deep learning based CTR(Click Through Rate) prediction models with PyTorch
Home-page: https://github.com/shenweichen/deepctr-torch
Author: Weichen Shen
Author-email: wcshen1994@163.com
License: Apache-2.0
Download-URL: https://github.com/shenweichen/deepctr-torch/tags
Keywords: ctr,click through rate,deep learning,torch,tensor,pytorch,deepctr
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*
Description-Content-Type: text/markdown
Requires-Dist: torch (>=1.1.0)
Requires-Dist: deepctr
Requires-Dist: tqdm
Requires-Dist: sklearn

# DeepCTR-Torch

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)](https://github.com/shenweichen/deepctr-torch/issues)


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PyTorch version of [DeepCTR](https://github.com/shenweichen/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!**](https://deepctr-torch.readthedocs.io/en/latest/Quick-Start.html)([Chinese Introduction](https://zhuanlan.zhihu.com/p/53231955))

## Contributors([welcome to join us!](./CONTRIBUTING.md))
<a href="https://github.com/shenweichen">
    <img src="https://avatars.githubusercontent.com/shenweichen " width=70 height="70" alt="pic" >
</a>
<a href="https://github.com/wutongzhang">
    <img src="https://avatars.githubusercontent.com/wutongzhang " width=70 height="70" alt="pic" >
</a>
<a href="https://github.com/JyiHUO">
    <img src="https://avatars.githubusercontent.com/JyiHUO " width=70 height="70" alt="pic" >
</a>
<a href="https://github.com/Zengai">
    <img src="https://avatars.githubusercontent.com/Zengai " width=70 height="70" alt="pic" >
</a>
<a href="https://github.com/chenkkkk">
    <img src="https://avatars.githubusercontent.com/chenkkkk " width=70 height="70" alt="pic" >
</a>
<a href="https://github.com/tangaqi">
    <img src="https://avatars.githubusercontent.com/tangaqi " width=70 height=70" alt="pic" >
</a>
<a href="https://github.com/uestc7d">
    <img src="https://avatars.githubusercontent.com/uestc7d " width=70 height="70" alt="pic" >
</a>


## Models List

|                 Model                  | Paper                                                                                                                                                           |
| :------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Convolutional Click Prediction Model | [CIKM 2015][A Convolutional Click Prediction Model](http://ir.ia.ac.cn/bitstream/173211/12337/1/A%20Convolutional%20Click%20Prediction%20Model.pdf)               |
| Factorization-supported Neural Network | [ECIR 2016][Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction](https://arxiv.org/pdf/1601.02376.pdf)                    |
|      Product-based Neural Network      | [ICDM 2016][Product-based neural networks for user response prediction](https://arxiv.org/pdf/1611.00144.pdf)                                                   |
|              Wide & Deep               | [DLRS 2016][Wide & Deep Learning for Recommender Systems](https://arxiv.org/pdf/1606.07792.pdf)                                                                 |
|                 DeepFM                 | [IJCAI 2017][DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](http://www.ijcai.org/proceedings/2017/0239.pdf)                           |
|        Piece-wise Linear Model         | [arxiv 2017][Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction](https://arxiv.org/abs/1704.05194)                                 |
|          Deep & Cross Network          | [ADKDD 2017][Deep & Cross Network for Ad Click Predictions](https://arxiv.org/abs/1708.05123)                                                                   |
|   Attentional Factorization Machine    | [IJCAI 2017][Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](http://www.ijcai.org/proceedings/2017/435) |
|      Neural Factorization Machine      | [SIGIR 2017][Neural Factorization Machines for Sparse Predictive Analytics](https://arxiv.org/pdf/1708.05027.pdf)                                               |
|                xDeepFM                 | [KDD 2018][xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://arxiv.org/pdf/1803.05170.pdf)                         |
|                AutoInt                 | [arxiv 2018][AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/abs/1810.11921)                              |
|                  ONN      | [arxiv 2019][Operation-aware Neural Networks for User Response Prediction](https://arxiv.org/pdf/1904.12579.pdf)                                                           |
|                  FiBiNET               | [RecSys 2019][FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction](https://arxiv.org/pdf/1905.09433.pdf)   |



