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deepctr-torch-0.2.9
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تبلیغات ما
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مشاهده بیشترتوضیحات
Easy-to-use,Modular and Extendible package of deep learning based CTR(Click Through Rate) prediction models with PyTorch
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | deepctr-torch-0.2.9 |
| نام | deepctr-torch |
| نسخه کتابخانه | 0.2.9 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Weichen Shen |
| ایمیل نویسنده | weichenswc@163.com |
| آدرس صفحه اصلی | https://github.com/shenweichen/deepctr-torch |
| آدرس اینترنتی | https://pypi.org/project/deepctr-torch/ |
| مجوز | Apache-2.0 |
# DeepCTR-Torch
[](https://pypi.org/project/deepctr-torch)
[](https://pepy.tech/project/deepctr-torch)
[](https://pypi.org/project/deepctr-torch)
[](https://github.com/shenweichen/deepctr-torch/issues)
[](https://deepctr-torch.readthedocs.io/)
[](https://codecov.io/gh/shenweichen/DeepCTR-Torch)
[](./README.md#disscussiongroup)
[](https://github.com/shenweichen/deepctr-torch/blob/master/LICENSE)
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))
## 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) |
| 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) |
| AutoInt | [CIKM 2019][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) |
| IFM | [IJCAI 2019][An Input-aware Factorization Machine for Sparse Prediction](https://www.ijcai.org/Proceedings/2019/0203.pdf) |
| DCN V2 | [arxiv 2020][DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://arxiv.org/abs/2008.13535) |
| DIFM | [IJCAI 2020][A Dual Input-aware Factorization Machine for CTR Prediction](https://www.ijcai.org/Proceedings/2020/0434.pdf) |
| AFN | [AAAI 2020][Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions](https://arxiv.org/pdf/1909.03276) |
| SharedBottom | [arxiv 2017][An Overview of Multi-Task Learning in Deep Neural Networks](https://arxiv.org/pdf/1706.05098.pdf) |
| ESMM | [SIGIR 2018][Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://dl.acm.org/doi/10.1145/3209978.3210104) |
| MMOE | [KDD 2018][Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts](https://dl.acm.org/doi/abs/10.1145/3219819.3220007) |
| PLE | [RecSys 2020][Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations](https://dl.acm.org/doi/10.1145/3383313.3412236) |
## DisscussionGroup & Related Projects
- [Github Discussions](https://github.com/shenweichen/DeepCTR/discussions)
- Wechat Discussions
|公众号：浅梦学习笔记|微信：deepctrbot|学习小组 [加入](https://t.zsxq.com/026UJEuzv) [主题集合](https://mp.weixin.qq.com/mp/appmsgalbum?__biz=MjM5MzY4NzE3MA==&action=getalbum&album_id=1361647041096843265&scene=126#wechat_redirect)|
|:--:|:--:|:--:|
| [](https://github.com/shenweichen/AlgoNotes)| [](https://github.com/shenweichen/AlgoNotes)|[](https://t.zsxq.com/026UJEuzv)|
- Related Projects
- [AlgoNotes](https://github.com/shenweichen/AlgoNotes)
- [DeepCTR](https://github.com/shenweichen/DeepCTR)
- [DeepMatch](https://github.com/shenweichen/DeepMatch)
- [GraphEmbedding](https://github.com/shenweichen/GraphEmbedding)
## Main Contributors([welcome to join us!](./CONTRIBUTING.md))
<table border="0">
<tbody>
<tr align="center" >
<td>
<a href="https://github.com/shenweichen"><img width="70" height="70" src="https://github.com/shenweichen.png?s=40" alt="pic"></a><br>
<a href="https://github.com/shenweichen">Shen Weichen</a>
<p> Alibaba Group </p>
</td>
<td>
<a href="https://github.com/zanshuxun"><img width="70" height="70" src="https://github.com/zanshuxun.png?s=40" alt="pic"></a><br>
<a href="https://github.com/zanshuxun">Zan Shuxun</a>
<p> Alibaba Group </p>
</td>
<td>
<a href="https://github.com/weberrr"><img width="70" height="70" src="https://github.com/weberrr.png?s=40" alt="pic"></a><br>
<a href="https://github.com/weberrr">Wang Ze</a>
<p> Meituan </p>
</td>
<td>
<a href="https://github.com/wutongzhang"><img width="70" height="70" src="https://github.com/wutongzhang.png?s=40" alt="pic"></a><br>
<a href="https://github.com/wutongzhang">Zhang Wutong</a>
<p> Tencent </p>
</td>
<td>
<a href="https://github.com/ZhangYuef"><img width="70" height="70" src="https://github.com/ZhangYuef.png?s=40" alt="pic"></a><br>
<a href="https://github.com/ZhangYuef">Zhang Yuefeng</a>
<p> Peking University </p>
</td>
</tr>
<tr align="center">
<td>
<a href="https://github.com/JyiHUO"><img width="70" height="70" src="https://github.com/JyiHUO.png?s=40" alt="pic"></a><br>
<a href="https://github.com/JyiHUO">Huo Junyi</a>
<p>
University of Southampton <br> <br> </p>
</td>
<td>
<a href="https://github.com/Zengai"><img width="70" height="70" src="https://github.com/Zengai.png?s=40" alt="pic"></a><br>
<a href="https://github.com/Zengai">Zeng Kai</a>
<p>
SenseTime <br> <br> </p>
</td>
<td>
<a href="https://github.com/chenkkkk"><img width="70" height="70" src="https://github.com/chenkkkk.png?s=40" alt="pic"></a><br>
<a href="https://github.com/chenkkkk">Chen K</a>
<p>
NetEase <br> <br> </p>
</td>
<td>
<a href="https://github.com/WeiyuCheng"><img width="70" height="70" src="https://github.com/WeiyuCheng.png?s=40" alt="pic"></a><br>
<a href="https://github.com/WeiyuCheng">Cheng Weiyu</a>
<p>
Shanghai Jiao Tong University</p>
</td>
<td>
<a href="https://github.com/tangaqi"><img width="70" height="70" src="https://github.com/tangaqi.png?s=40" alt="pic"></a><br>
<a href="https://github.com/tangaqi">Tang</a>
<p>
Tongji University <br> <br> </p>
</td>
</tr>
</tbody>
</table>
نیازمندی
| مقدار | نام |
|---|---|
| >=1.2.0 | torch |
| - | tqdm |
| - | scikit-learn |
| - | tensorflow |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.* | Python |
نحوه نصب
نصب پکیج whl deepctr-torch-0.2.9:
pip install deepctr-torch-0.2.9.whl
نصب پکیج tar.gz deepctr-torch-0.2.9:
pip install deepctr-torch-0.2.9.tar.gz