Package: deepctr
DeepCTRDeepCTR is aEasy-to-use,ModularandExtendiblepackage 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 withmodel.fit()，andmodel.predict().Providetf.keras.Modellike interfaces forquick experiment.exampleProvidetensorflow estimatorinterface forlarge scale dataanddistributed training.exampleIt is compatible with bothtf 1.xandtf 2.x.Some related projects:DeepMatch:https://github.com/shenweichen/DeepMatchDeepCTR-Torch:https://github.com/shenweichen/DeepCTR-TorchLet'sGet Started!(Chinese
Introduction) andwelcome to join us!Models ListModelPaperConvolutional Click Prediction Model[CIKM 2015]A Convolutional Click Prediction ModelFactorization-supported Neural Network[ECIR 2016]Deep Learning over Multi-field Categorical Data: A Case Study on User Response PredictionProduct-based Neural Network[ICDM 2016]Product-based neural networks for user response predictionWide & Deep[DLRS 2016]Wide & Deep Learning for Recommender SystemsDeepFM[IJCAI 2017]DeepFM: A Factorization-Machine based Neural Network for CTR PredictionPiece-wise Linear Model[arxiv 2017]Learning Piece-wise Linear Models from Large Scale Data for Ad Click PredictionDeep & Cross Network[ADKDD 2017]Deep & Cross Network for Ad Click PredictionsAttentional Factorization Machine[IJCAI 2017]Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention NetworksNeural Factorization Machine[SIGIR 2017]Neural Factorization Machines for Sparse Predictive AnalyticsxDeepFM[KDD 2018]xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender SystemsDeep Interest Network[KDD 2018]Deep Interest Network for Click-Through Rate PredictionAutoInt[CIKM 2019]AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural NetworksDeep Interest Evolution Network[AAAI 2019]Deep Interest Evolution Network for Click-Through Rate PredictionFwFM[WWW 2018]Field-weighted Factorization Machines for Click-Through Rate Prediction in Display AdvertisingONN[arxiv 2019]Operation-aware Neural Networks for User Response PredictionFGCNN[WWW 2019]Feature Generation by Convolutional Neural Network for Click-Through Rate PredictionDeep Session Interest Network[IJCAI 2019]Deep Session Interest Network for Click-Through Rate PredictionFiBiNET[RecSys 2019]FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate PredictionFLEN[arxiv 2019]FLEN: Leveraging Field for Scalable CTR PredictionBST[DLP-KDD 2019]Behavior sequence transformer for e-commerce recommendation in AlibabaIFM[IJCAI 2019]An Input-aware Factorization Machine for Sparse PredictionDCN V2[arxiv 2020]DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank SystemsDIFM[IJCAI 2020]A Dual Input-aware Factorization Machine for CTR PredictionFEFM and DeepFEFM[arxiv 2020]Field-Embedded Factorization Machines for Click-through rate predictionSharedBottom[arxiv 2017]An Overview of Multi-Task Learning in Deep Neural NetworksESMM[SIGIR 2018]Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion RateMMOE[KDD 2018]Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-ExpertsPLE[RecSys 2020]Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized RecommendationsEDCN[KDD 2021]Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR ModelsCitationWeichen 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}},}DisscussionGroupGithub DiscussionsWechat Discussions公众号：浅梦学习笔记微信：deepctrbot学习小组加入主题集合Main contributors(welcome to join us!)​​Shen Weichen​Alibaba Group​Zan Shuxun​Alibaba Group​​​Harshit PandeAmazon​​​Lai MincaiByteDance​​​Li ZichaoByteDance​​Tan TingyiChongqing Universityof  Posts andTelecommunications​