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| author | CoprDistGit <infra@openeuler.org> | 2023-05-18 06:08:25 +0000 |
|---|---|---|
| committer | CoprDistGit <infra@openeuler.org> | 2023-05-18 06:08:25 +0000 |
| commit | 1f07710f2c347a61c0405fe54dcebf7c696450d1 (patch) | |
| tree | 3cd2a5b9aa53bbcb787e106089bf1832feaea9f8 | |
| parent | 490f2158ce397a3b26b63f236d802800be4f0515 (diff) | |
automatic import of python-deepctr-torch
| -rw-r--r-- | .gitignore | 1 | ||||
| -rw-r--r-- | python-deepctr-torch.spec | 475 | ||||
| -rw-r--r-- | sources | 1 |
3 files changed, 477 insertions, 0 deletions
@@ -0,0 +1 @@ +/deepctr-torch-0.2.9.tar.gz diff --git a/python-deepctr-torch.spec b/python-deepctr-torch.spec new file mode 100644 index 0000000..4f4b5d8 --- /dev/null +++ b/python-deepctr-torch.spec @@ -0,0 +1,475 @@ +%global _empty_manifest_terminate_build 0 +Name: python-deepctr-torch +Version: 0.2.9 +Release: 1 +Summary: Easy-to-use,Modular and Extendible package of deep learning based CTR(Click Through Rate) prediction models with PyTorch +License: Apache-2.0 +URL: https://github.com/shenweichen/deepctr-torch +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/29/47/9d383aaef8838b7d0707c7fb8444f4a0f87a3330ae8fb25fa0591d0e16cc/deepctr-torch-0.2.9.tar.gz +BuildArch: noarch + +Requires: python3-torch +Requires: python3-tqdm +Requires: python3-scikit-learn +Requires: python3-tensorflow + +%description +# 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> + + + +%package -n python3-deepctr-torch +Summary: Easy-to-use,Modular and Extendible package of deep learning based CTR(Click Through Rate) prediction models with PyTorch +Provides: python-deepctr-torch +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-deepctr-torch +# 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> + + + +%package help +Summary: Development documents and examples for deepctr-torch +Provides: python3-deepctr-torch-doc +%description help +# 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> + + + +%prep +%autosetup -n deepctr-torch-0.2.9 + +%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-torch -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Thu May 18 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.9-1 +- Package Spec generated @@ -0,0 +1 @@ +c0ef6da565f37171d41d1da2d491ab01 deepctr-torch-0.2.9.tar.gz |