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diff --git a/python-deepctr.spec b/python-deepctr.spec new file mode 100644 index 0000000..6332454 --- /dev/null +++ b/python-deepctr.spec @@ -0,0 +1,491 @@ +%global _empty_manifest_terminate_build 0 +Name: python-deepctr +Version: 0.9.3 +Release: 1 +Summary: Easy-to-use,Modular and Extendible package of deep learning based CTR(Click Through Rate) prediction models with tensorflow 1.x and 2.x . +License: Apache-2.0 +URL: https://github.com/shenweichen/deepctr +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/32/e6/a0c65da46ce3c224bf5c468487a307ba074e0df223825a8a00e1474f9081/deepctr-0.9.3.tar.gz +BuildArch: noarch + +Requires: python3-requests +Requires: python3-h5py +Requires: python3-h5py +Requires: python3-tensorflow +Requires: python3-tensorflow-gpu + +%description +# DeepCTR + +[](https://pypi.org/project/deepctr) +[](https://pypi.org/project/deepctr) +[](https://pepy.tech/project/deepctr) +[](https://pypi.org/project/deepctr) +[](https://github.com/shenweichen/deepctr/issues) +<!-- [](https://github.com/shenweichen/DeepCTR/commits/master) --> + + +[](https://deepctr-doc.readthedocs.io/) + +[](https://codecov.io/gh/shenweichen/DeepCTR) +[](https://www.codacy.com/gh/shenweichen/DeepCTR?utm_source=github.com&utm_medium=referral&utm_content=shenweichen/DeepCTR&utm_campaign=Badge_Grade) +[](./README.md#DisscussionGroup) +[](https://github.com/shenweichen/deepctr/blob/master/LICENSE) +<!-- [](https://gitter.im/DeepCTR/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) --> + + +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](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`. + +Some related projects: + +- DeepMatch: https://github.com/shenweichen/DeepMatch +- DeepCTR-Torch: https://github.com/shenweichen/DeepCTR-Torch + +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) | +| Deep Interest Network | [KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1706.06978.pdf) | +| AutoInt | [CIKM 2019][AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/abs/1810.11921) | +| 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) | +| BST | [DLP-KDD 2019][Behavior sequence transformer for e-commerce recommendation in Alibaba](https://arxiv.org/pdf/1905.06874.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) | +| FEFM and DeepFEFM | [arxiv 2020][Field-Embedded Factorization Machines for Click-through rate prediction](https://arxiv.org/abs/2009.09931) | +| 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://arxiv.org/abs/1804.07931) | +| 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) | +| EDCN | [KDD 2021][Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models](https://dlp-kdd.github.io/assets/pdf/DLP-KDD_2021_paper_12.pdf) | + +## 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: + +```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](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)| + +## 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/pandeconscious"><img width="70" height="70" src="https://github.com/pandeconscious.png?s=40" alt="pic"></a><br> + <a href="https://github.com/pandeconscious">Harshit Pande</a> + <p> Amazon </p> + </td> + <td> + <a href="https://github.com/morningsky"><img width="70" height="70" src="https://github.com/morningsky.png?s=40" alt="pic"></a><br> + <a href="https://github.com/morningsky">Lai Mincai</a> + <p> ByteDance </p> + </td> + <td> + <a href="https://github.com/codewithzichao"><img width="70" height="70" src="https://github.com/codewithzichao.png?s=40" alt="pic"></a><br> + <a href="https://github.com/codewithzichao">Li Zichao</a> + <p> ByteDance </p> + </td> + <td> + <a href="https://github.com/TanTingyi"><img width="70" height="70" src="https://github.com/TanTingyi.png?s=40" alt="pic"></a><br> + <a href="https://github.com/TanTingyi">Tan Tingyi</a> + <p> Chongqing University <br> of Posts and <br> Telecommunications </p> + </td> + </tr> + </tbody> +</table> + + + + +%package -n python3-deepctr +Summary: Easy-to-use,Modular and Extendible package of deep learning based CTR(Click Through Rate) prediction models with tensorflow 1.x and 2.x . +Provides: python-deepctr +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-deepctr +# DeepCTR + +[](https://pypi.org/project/deepctr) +[](https://pypi.org/project/deepctr) +[](https://pepy.tech/project/deepctr) +[](https://pypi.org/project/deepctr) +[](https://github.com/shenweichen/deepctr/issues) +<!-- [](https://github.com/shenweichen/DeepCTR/commits/master) --> + + +[](https://deepctr-doc.readthedocs.io/) + +[](https://codecov.io/gh/shenweichen/DeepCTR) +[](https://www.codacy.com/gh/shenweichen/DeepCTR?utm_source=github.com&utm_medium=referral&utm_content=shenweichen/DeepCTR&utm_campaign=Badge_Grade) +[](./README.md#DisscussionGroup) +[](https://github.com/shenweichen/deepctr/blob/master/LICENSE) +<!-- [](https://gitter.im/DeepCTR/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) --> + + +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](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`. + +Some related projects: + +- DeepMatch: https://github.com/shenweichen/DeepMatch +- DeepCTR-Torch: https://github.com/shenweichen/DeepCTR-Torch + +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) | +| Deep Interest Network | [KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1706.06978.pdf) | +| AutoInt | [CIKM 2019][AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/abs/1810.11921) | +| 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) | +| BST | [DLP-KDD 2019][Behavior sequence transformer for e-commerce recommendation in Alibaba](https://arxiv.org/pdf/1905.06874.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) | +| FEFM and DeepFEFM | [arxiv 2020][Field-Embedded Factorization Machines for Click-through rate prediction](https://arxiv.org/abs/2009.09931) | +| 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://arxiv.org/abs/1804.07931) | +| 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) | +| EDCN | [KDD 2021][Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models](https://dlp-kdd.github.io/assets/pdf/DLP-KDD_2021_paper_12.pdf) | + +## 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: + +```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](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)| + +## 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/pandeconscious"><img width="70" height="70" src="https://github.com/pandeconscious.png?s=40" alt="pic"></a><br> + <a href="https://github.com/pandeconscious">Harshit Pande</a> + <p> Amazon </p> + </td> + <td> + <a href="https://github.com/morningsky"><img width="70" height="70" src="https://github.com/morningsky.png?s=40" alt="pic"></a><br> + <a href="https://github.com/morningsky">Lai Mincai</a> + <p> ByteDance </p> + </td> + <td> + <a href="https://github.com/codewithzichao"><img width="70" height="70" src="https://github.com/codewithzichao.png?s=40" alt="pic"></a><br> + <a href="https://github.com/codewithzichao">Li Zichao</a> + <p> ByteDance </p> + </td> + <td> + <a href="https://github.com/TanTingyi"><img width="70" height="70" src="https://github.com/TanTingyi.png?s=40" alt="pic"></a><br> + <a href="https://github.com/TanTingyi">Tan Tingyi</a> + <p> Chongqing University <br> of Posts and <br> Telecommunications </p> + </td> + </tr> + </tbody> +</table> + + + + +%package help +Summary: Development documents and examples for deepctr +Provides: python3-deepctr-doc +%description help +# DeepCTR + +[](https://pypi.org/project/deepctr) +[](https://pypi.org/project/deepctr) +[](https://pepy.tech/project/deepctr) +[](https://pypi.org/project/deepctr) +[](https://github.com/shenweichen/deepctr/issues) +<!-- [](https://github.com/shenweichen/DeepCTR/commits/master) --> + + +[](https://deepctr-doc.readthedocs.io/) + +[](https://codecov.io/gh/shenweichen/DeepCTR) +[](https://www.codacy.com/gh/shenweichen/DeepCTR?utm_source=github.com&utm_medium=referral&utm_content=shenweichen/DeepCTR&utm_campaign=Badge_Grade) +[](./README.md#DisscussionGroup) +[](https://github.com/shenweichen/deepctr/blob/master/LICENSE) +<!-- [](https://gitter.im/DeepCTR/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) --> + + +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](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`. + +Some related projects: + +- DeepMatch: https://github.com/shenweichen/DeepMatch +- DeepCTR-Torch: https://github.com/shenweichen/DeepCTR-Torch + +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) | +| Deep Interest Network | [KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1706.06978.pdf) | +| AutoInt | [CIKM 2019][AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/abs/1810.11921) | +| 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) | +| BST | [DLP-KDD 2019][Behavior sequence transformer for e-commerce recommendation in Alibaba](https://arxiv.org/pdf/1905.06874.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) | +| FEFM and DeepFEFM | [arxiv 2020][Field-Embedded Factorization Machines for Click-through rate prediction](https://arxiv.org/abs/2009.09931) | +| 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://arxiv.org/abs/1804.07931) | +| 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) | +| EDCN | [KDD 2021][Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models](https://dlp-kdd.github.io/assets/pdf/DLP-KDD_2021_paper_12.pdf) | + +## 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: + +```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](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)| + +## 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/pandeconscious"><img width="70" height="70" src="https://github.com/pandeconscious.png?s=40" alt="pic"></a><br> + <a href="https://github.com/pandeconscious">Harshit Pande</a> + <p> Amazon </p> + </td> + <td> + <a href="https://github.com/morningsky"><img width="70" height="70" src="https://github.com/morningsky.png?s=40" alt="pic"></a><br> + <a href="https://github.com/morningsky">Lai Mincai</a> + <p> ByteDance </p> + </td> + <td> + <a href="https://github.com/codewithzichao"><img width="70" height="70" src="https://github.com/codewithzichao.png?s=40" alt="pic"></a><br> + <a href="https://github.com/codewithzichao">Li Zichao</a> + <p> ByteDance </p> + </td> + <td> + <a href="https://github.com/TanTingyi"><img width="70" height="70" src="https://github.com/TanTingyi.png?s=40" alt="pic"></a><br> + <a href="https://github.com/TanTingyi">Tan Tingyi</a> + <p> Chongqing University <br> of Posts and <br> Telecommunications </p> + </td> + </tr> + </tbody> +</table> + + + + +%prep +%autosetup -n deepctr-0.9.3 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-deepctr -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Wed Apr 12 2023 Python_Bot <Python_Bot@openeuler.org> - 0.9.3-1 +- Package Spec generated |