%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 [![Python Versions](https://img.shields.io/pypi/pyversions/deepctr.svg)](https://pypi.org/project/deepctr) [![TensorFlow Versions](https://img.shields.io/badge/TensorFlow-1.4+/2.0+-blue.svg)](https://pypi.org/project/deepctr) [![Downloads](https://pepy.tech/badge/deepctr)](https://pepy.tech/project/deepctr) [![PyPI Version](https://img.shields.io/pypi/v/deepctr.svg)](https://pypi.org/project/deepctr) [![GitHub Issues](https://img.shields.io/github/issues/shenweichen/deepctr.svg )](https://github.com/shenweichen/deepctr/issues) [![Documentation Status](https://readthedocs.org/projects/deepctr-doc/badge/?version=latest)](https://deepctr-doc.readthedocs.io/) ![CI status](https://github.com/shenweichen/deepctr/workflows/CI/badge.svg) [![codecov](https://codecov.io/gh/shenweichen/DeepCTR/branch/master/graph/badge.svg)](https://codecov.io/gh/shenweichen/DeepCTR) [![Codacy Badge](https://api.codacy.com/project/badge/Grade/d4099734dc0e4bab91d332ead8c0bdd0)](https://www.codacy.com/gh/shenweichen/DeepCTR?utm_source=github.com&utm_medium=referral&utm_content=shenweichen/DeepCTR&utm_campaign=Badge_Grade) [![Disscussion](https://img.shields.io/badge/chat-wechat-brightgreen?style=flat)](./README.md#DisscussionGroup) [![License](https://img.shields.io/github/license/shenweichen/deepctr.svg)](https://github.com/shenweichen/deepctr/blob/master/LICENSE) 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)| |:--:|:--:|:--:| | [![公众号](./docs/pics/code.png)](https://github.com/shenweichen/AlgoNotes)| [![微信](./docs/pics/deepctrbot.png)](https://github.com/shenweichen/AlgoNotes)|[![学习小组](./docs/pics/planet_github.png)](https://t.zsxq.com/026UJEuzv)| ## Main contributors([welcome to join us!](./CONTRIBUTING.md))
​ pic
​ Shen Weichen ​

Alibaba Group

​ 		pic
Zan Shuxun ​

Alibaba Group

​ 		​ pic
​ Harshit Pande

Amazon

​ 		​ pic
​ Lai Mincai

ByteDance

​ 		​ pic
​ Li Zichao

ByteDance

​ 		​ pic
Tan Tingyi

Chongqing University
of Posts and
Telecommunications

​
%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 [![Python Versions](https://img.shields.io/pypi/pyversions/deepctr.svg)](https://pypi.org/project/deepctr) [![TensorFlow Versions](https://img.shields.io/badge/TensorFlow-1.4+/2.0+-blue.svg)](https://pypi.org/project/deepctr) [![Downloads](https://pepy.tech/badge/deepctr)](https://pepy.tech/project/deepctr) [![PyPI Version](https://img.shields.io/pypi/v/deepctr.svg)](https://pypi.org/project/deepctr) [![GitHub Issues](https://img.shields.io/github/issues/shenweichen/deepctr.svg )](https://github.com/shenweichen/deepctr/issues) [![Documentation Status](https://readthedocs.org/projects/deepctr-doc/badge/?version=latest)](https://deepctr-doc.readthedocs.io/) ![CI status](https://github.com/shenweichen/deepctr/workflows/CI/badge.svg) [![codecov](https://codecov.io/gh/shenweichen/DeepCTR/branch/master/graph/badge.svg)](https://codecov.io/gh/shenweichen/DeepCTR) [![Codacy Badge](https://api.codacy.com/project/badge/Grade/d4099734dc0e4bab91d332ead8c0bdd0)](https://www.codacy.com/gh/shenweichen/DeepCTR?utm_source=github.com&utm_medium=referral&utm_content=shenweichen/DeepCTR&utm_campaign=Badge_Grade) [![Disscussion](https://img.shields.io/badge/chat-wechat-brightgreen?style=flat)](./README.md#DisscussionGroup) [![License](https://img.shields.io/github/license/shenweichen/deepctr.svg)](https://github.com/shenweichen/deepctr/blob/master/LICENSE) 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)| |:--:|:--:|:--:| | [![公众号](./docs/pics/code.png)](https://github.com/shenweichen/AlgoNotes)| [![微信](./docs/pics/deepctrbot.png)](https://github.com/shenweichen/AlgoNotes)|[![学习小组](./docs/pics/planet_github.png)](https://t.zsxq.com/026UJEuzv)| ## Main contributors([welcome to join us!](./CONTRIBUTING.md))
​ pic
​ Shen Weichen ​

Alibaba Group

​ 		pic
Zan Shuxun ​

Alibaba Group

​ 		​ pic
​ Harshit Pande

Amazon

​ 		​ pic
​ Lai Mincai

ByteDance

​ 		​ pic
​ Li Zichao

ByteDance

​ 		​ pic
Tan Tingyi

Chongqing University
of Posts and
Telecommunications

​
%package help Summary: Development documents and examples for deepctr Provides: python3-deepctr-doc %description help # DeepCTR [![Python Versions](https://img.shields.io/pypi/pyversions/deepctr.svg)](https://pypi.org/project/deepctr) [![TensorFlow Versions](https://img.shields.io/badge/TensorFlow-1.4+/2.0+-blue.svg)](https://pypi.org/project/deepctr) [![Downloads](https://pepy.tech/badge/deepctr)](https://pepy.tech/project/deepctr) [![PyPI Version](https://img.shields.io/pypi/v/deepctr.svg)](https://pypi.org/project/deepctr) [![GitHub Issues](https://img.shields.io/github/issues/shenweichen/deepctr.svg )](https://github.com/shenweichen/deepctr/issues) [![Documentation Status](https://readthedocs.org/projects/deepctr-doc/badge/?version=latest)](https://deepctr-doc.readthedocs.io/) ![CI status](https://github.com/shenweichen/deepctr/workflows/CI/badge.svg) [![codecov](https://codecov.io/gh/shenweichen/DeepCTR/branch/master/graph/badge.svg)](https://codecov.io/gh/shenweichen/DeepCTR) [![Codacy Badge](https://api.codacy.com/project/badge/Grade/d4099734dc0e4bab91d332ead8c0bdd0)](https://www.codacy.com/gh/shenweichen/DeepCTR?utm_source=github.com&utm_medium=referral&utm_content=shenweichen/DeepCTR&utm_campaign=Badge_Grade) [![Disscussion](https://img.shields.io/badge/chat-wechat-brightgreen?style=flat)](./README.md#DisscussionGroup) [![License](https://img.shields.io/github/license/shenweichen/deepctr.svg)](https://github.com/shenweichen/deepctr/blob/master/LICENSE) 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)| |:--:|:--:|:--:| | [![公众号](./docs/pics/code.png)](https://github.com/shenweichen/AlgoNotes)| [![微信](./docs/pics/deepctrbot.png)](https://github.com/shenweichen/AlgoNotes)|[![学习小组](./docs/pics/planet_github.png)](https://t.zsxq.com/026UJEuzv)| ## Main contributors([welcome to join us!](./CONTRIBUTING.md))
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​ Shen Weichen ​

Alibaba Group

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Zan Shuxun ​

Alibaba Group

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​ Harshit Pande

Amazon

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​ Lai Mincai

ByteDance

​ 		​ pic
​ Li Zichao

ByteDance

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Tan Tingyi

Chongqing University
of Posts and
Telecommunications

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%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 - 0.9.3-1 - Package Spec generated