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This project is under development and we need developers to participate in.
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# Join us
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If you
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- familiar with and interested in ctr prediction algorithms
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- familiar with tensorflow
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- have spare time to learn and develop
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- familiar with git
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please send a brief introduction of your background and experience to [email protected], welcome to join us!
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please send a brief introduction of your background and experience to [email protected], welcome to join us!
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# Creating a pull request
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1.**Become a collaborator**: Send an email with introduction and your github account name to [email protected] and waiting for invitation to become a collaborator.
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2.**Fork&Dev**: Fork your own branch(`dev_yourname`) in `DeepCTR` from the `master` branch for development.If the `master` is updated during the development process, remember to merge and update to `dev_yourname` regularly.
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3.**Testing**: Test logical correctness and effect when finishing the code development of the `dev_yourname` branch.
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4.**Pre-release** : After testing contact [email protected] for pre-release integration, usually your branch `dev_yourname` will be merged into `release` branch by squash merge.
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5.**Release a new version**: After confirming that the change is no longer needed, `release` branch will be merged into `master` and a new python package will be released on pypi.
| xDeepFM |[KDD 2018][xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://arxiv.org/pdf/1803.05170.pdf)|
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|AutoInt|[arxiv 2018][AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/abs/1810.11921)|
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|Deep Interest Network|[KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1706.06978.pdf)|
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| Deep Interest Network | [KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1706.06978.pdf)
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|AutoInt|[CIKM 2019][AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/abs/1810.11921)||
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| Deep Interest Evolution Network |[AAAI 2019][Deep Interest Evolution Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1809.03672.pdf)|
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| FwFM |[WWW 2018][Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising](https://arxiv.org/pdf/1806.03514.pdf)|
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| ONN |[arxiv 2019][Operation-aware Neural Networks for User Response Prediction](https://arxiv.org/pdf/1904.12579.pdf)|
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| FGCNN |[WWW 2019][Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction ](https://arxiv.org/pdf/1904.04447)|
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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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| 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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@@ -75,7 +76,7 @@ If you find this code useful in your research, please cite it using the followin
[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.
[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.
[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)
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