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v0.8.5
- Implement the BST model . - Update Transformer Layer, support new attention_type and output_type .
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‎.travis.yml‎

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notifications:
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recipients:
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- wcshen1994@163.com
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- weichenswc@163.com
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‎README.md‎

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[![Documentation Status](https://readthedocs.org/projects/deepctr-doc/badge/?version=latest)](https://deepctr-doc.readthedocs.io/)
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![CI status](https://github.com/shenweichen/deepctr/workflows/CI/badge.svg)
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[![Coverage Status](https://coveralls.io/repos/github/shenweichen/DeepCTR/badge.svg?branch=master)](https://coveralls.io/github/shenweichen/DeepCTR?branch=master)
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[![Codacy Badge](https://api.codacy.com/project/badge/Grade/d4099734dc0e4bab91d332ead8c0bdd0)](https://www.codacy.com/app/wcshen1994/DeepCTR?utm_source=github.com&utm_medium=referral&utm_content=shenweichen/DeepCTR&utm_campaign=Badge_Grade)
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[![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)
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[![Disscussion](https://img.shields.io/badge/chat-wechat-brightgreen?style=flat)](./README.md#DisscussionGroup)
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[![License](https://img.shields.io/github/license/shenweichen/deepctr.svg)](https://github.com/shenweichen/deepctr/blob/master/LICENSE)
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<!-- [![Gitter](https://badges.gitter.im/DeepCTR/community.svg)](https://gitter.im/DeepCTR/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) -->
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| Deep Session Interest Network | [IJCAI 2019][Deep Session Interest Network for Click-Through Rate Prediction ](https://arxiv.org/abs/1905.06482) |
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| FiBiNET | [RecSys 2019][FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction](https://arxiv.org/pdf/1905.09433.pdf) |
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| FLEN | [arxiv 2019][FLEN: Leveraging Field for Scalable CTR Prediction](https://arxiv.org/pdf/1911.04690.pdf) |
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| BST | [DLP-KDD 2019][Behavior sequence transformer for e-commerce recommendation in Alibaba](https://arxiv.org/pdf/1905.06874.pdf) |
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| 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) |
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## Citation
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![wechat](./docs/pics/code.png)
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## Main contributors([welcome to join us!](./CONTRIBUTING.md))
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<table border="0">
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<tbody>
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<tr align="center" >
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<td>
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​ <a href="https://github.com/shenweichen"><img width="70" height="70" src="https://github.com/shenweichen.png?s=40" alt="pic"></a><br>
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​ <a href="https://github.com/shenweichen">Shen Weichen</a> ​
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<p>
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Alibaba Group </p>​
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</td>
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<td>
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<a href="https://github.com/zanshuxun"><img width="70" height="70" src="https://github.com/zanshuxun.png?s=40" alt="pic"></a><br>
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<a href="https://github.com/zanshuxun">Zan Shuxun</a> ​
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<p>Beijing University <br> of Posts and <br> Telecommunications </p>​
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</td>
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<td>
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​ <a href="https://github.com/pandeconscious"><img width="70" height="70" src="https://github.com/pandeconscious.png?s=40" alt="pic"></a><br>
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​ <a href="https://github.com/pandeconscious">Harshit Pande</a>
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<p> Amazon </p>​
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</td>
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<td>
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​ <a href="https://github.com/codewithzichao"><img width="70" height="70" src="https://github.com/codewithzichao.png?s=40" alt="pic"></a><br>
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​ <a href="https://github.com/codewithzichao">Li Zichao</a>
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<p> Peking University </p>​
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</td>
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<td>
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​ <a href="https://github.com/TanTingyi"><img width="70" height="70" src="https://github.com/TanTingyi.png?s=40" alt="pic"></a><br>
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<a href="https://github.com/TanTingyi">LeoCai</a>
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<p> Chongqing University <br> of Posts and <br> Telecommunications </p>​
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</td>
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</tr>
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</tbody>
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</table>

‎deepctr/__init__.py‎

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from .utils import check_version
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__version__ = '0.8.3'
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check_version(__version__)
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from .utils import check_version
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__version__ = '0.8.5'
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check_version(__version__)

‎deepctr/estimator/models/afm.py‎

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Author:
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Reference:
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[1] Xiao J, Ye H, He X, et al. Attentional factorization machines: Learning the weight of feature interactions via attention networks[J]. arXiv preprint arXiv:1708.04617, 2017.

‎deepctr/estimator/models/autoint.py‎

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[1] Song W, Shi C, Xiao Z, et al. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks[J]. arXiv preprint arXiv:1810.11921, 2018.(https://arxiv.org/abs/1810.11921)

‎deepctr/estimator/models/ccpm.py‎

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[1] Liu Q, Yu F, Wu S, et al. A convolutional click prediction model[C]//Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. ACM, 2015: 1743-1746.

‎deepctr/estimator/models/dcn.py‎

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[1] Wang R, Fu B, Fu G, et al. Deep & cross network for ad click predictions[C]//Proceedings of the ADKDD'17. ACM, 2017: 12. (https://arxiv.org/abs/1708.05123)

‎deepctr/estimator/models/deepfm.py‎

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[1] Guo H, Tang R, Ye Y, et al. Deepfm: a factorization-machine based neural network for ctr prediction[J]. arXiv preprint arXiv:1703.04247, 2017.(https://arxiv.org/abs/1703.04247)

‎deepctr/estimator/models/fibinet.py‎

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[1] Huang T, Zhang Z, Zhang J. FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction[J]. arXiv preprint arXiv:1905.09433, 2019.

‎deepctr/estimator/models/fnn.py‎

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[1] Zhang W, Du T, Wang J. Deep learning over multi-field categorical data[C]//European conference on information retrieval. Springer, Cham, 2016: 45-57.(https://arxiv.org/pdf/1601.02376.pdf)

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