deep ctr torch 源代码

# 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>​