DeepCTR

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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
• Provide tensorflow estimator interface for large scale data and distributed training. example
• 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!(Chinese Introduction) and welcome to join us!

Models List

| Model | Paper | | :------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Convolutional Click Prediction Model | [CIKM 2015]A Convolutional Click Prediction Model | | Factorization-supported Neural Network | [ECIR 2016]Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction | | Product-based Neural Network | [ICDM 2016]Product-based neural networks for user response prediction | | Wide & Deep | [DLRS 2016]Wide & Deep Learning for Recommender Systems | | DeepFM | [IJCAI 2017]DeepFM: A Factorization-Machine based Neural Network for CTR Prediction | | Piece-wise Linear Model | [arxiv 2017]Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction | | Deep & Cross Network | [ADKDD 2017]Deep & Cross Network for Ad Click Predictions | | Attentional Factorization Machine | [IJCAI 2017]Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks | | Neural Factorization Machine | [SIGIR 2017]Neural Factorization Machines for Sparse Predictive Analytics | | xDeepFM | [KDD 2018]xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems | | Deep Interest Network | [KDD 2018]Deep Interest Network for Click-Through Rate Prediction | | AutoInt | [CIKM 2019]AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks | | Deep Interest Evolution Network | [AAAI 2019]Deep Interest Evolution Network for Click-Through Rate Prediction | | FwFM | [WWW 2018]Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising | | ONN | [arxiv 2019]Operation-aware Neural Networks for User Response Prediction | | FGCNN | [WWW 2019]Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction | | Deep Session Interest Network | [IJCAI 2019]Deep Session Interest Network for Click-Through Rate Prediction | | FiBiNET | [RecSys 2019]FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction | | FLEN | [arxiv 2019]FLEN: Leveraging Field for Scalable CTR Prediction | | BST | [DLP-KDD 2019]Behavior sequence transformer for e-commerce recommendation in Alibaba | | IFM | [IJCAI 2019]An Input-aware Factorization Machine for Sparse Prediction | | DCN V2 | [arxiv 2020]DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems | | DIFM | [IJCAI 2020]A Dual Input-aware Factorization Machine for CTR Prediction | | FEFM and DeepFEFM | [arxiv 2020]Field-Embedded Factorization Machines for Click-through rate prediction | | SharedBottom | [arxiv 2017]An Overview of Multi-Task Learning in Deep Neural Networks | | ESMM | [SIGIR 2018]Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate | | MMOE | [KDD 2018]Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts | | PLE | [RecSys 2020]Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations | | EDCN | [KDD 2021]Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models |

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:

@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
• Wechat Discussions

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