npcnn 0.3
test code based on deepctr
License: Apache-2.0
Requires Python: >=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*
Keywords: ctr,click through rate,deep learning,tensorflow,tensor,keras

# DeepCTR

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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 interface for **quick experiment**. [example](https://deepctr-doc.readthedocs.io/en/latest/Quick-Start.html#getting-started-4-steps-to-deepctr)
- Provide  `tensorflow estimator` interface for **large scale data** and **distributed training**. [example](https://deepctr-doc.readthedocs.io/en/latest/Quick-Start.html#getting-started-4-steps-to-deepctr-estimator-with-tfrecord)
- It is compatible with both `tf 1.x`  and `tf 2.x`.




Let's [**Get Started!**](https://deepctr-doc.readthedocs.io/en/latest/Quick-Start.html)([Chinese Introduction](https://zhuanlan.zhihu.com/p/53231955)) and [welcome to join us!](./CONTRIBUTING.md)

## 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)                              |
|         Deep Interest Network          | [KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1706.06978.pdf)                                                       |
|    Deep Interest Evolution Network     | [AAAI 2019][Deep Interest Evolution Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1809.03672.pdf)                                            |
|                FwFM                    | [WWW 2018][Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising](https://arxiv.org/pdf/1806.03514.pdf)                |
|                  ONN                  | [arxiv 2019][Operation-aware Neural Networks for User Response Prediction](https://arxiv.org/pdf/1904.12579.pdf)                                                |
|                 FGCNN                  | [WWW 2019][Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction ](https://arxiv.org/pdf/1904.04447)                             |
|     Deep Session Interest Network      | [IJCAI 2019][Deep Session Interest Network for Click-Through Rate Prediction ](https://arxiv.org/abs/1905.06482)                                                |
|                FiBiNET                 | [RecSys 2019][FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction](https://arxiv.org/pdf/1905.09433.pdf)   |
|                FLEN                    | [arxiv 2019][FLEN: Leveraging Field for Scalable CTR Prediction](https://arxiv.org/pdf/1911.04690.pdf)   |

## Citation

- Weichen Shen. (2018). 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:

```bibtex
@misc{shen2018deepctr,
  author = {Weichen Shen},
  title = {DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR models},
  year = {2018},
  publisher = {GitHub},
  journal = {GitHub Repository},
  howpublished = {\url{https://github.com/shenweichen/deepctr}},
}
```

## DisscussionGroup 交流群

Please follow our wechat to join group:  
- 公众号：**浅梦的学习笔记**  
- wechat ID: **deepctrbot**

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