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shenweichen / DeepCTR-Torch

【PyTorch】Easy-to-use,Modular and Extendible package of deep-learning based CTR models.
https://deepctr-torch.readthedocs.io/en/latest/index.html
Apache License 2.0
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ctr-models deep-learning deepctr deepctr-pytorch deepfm deeprec fibinet torchrec xdeepfm

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DeepCTR-Torch

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PyTorch version of 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!(Chinese Introduction)

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
Deep Interest Evolution Network [AAAI 2019]Deep Interest Evolution Network for Click-Through Rate Prediction
AutoInt [CIKM 2019]AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
ONN [arxiv 2019]Operation-aware Neural Networks for User Response Prediction
FiBiNET [RecSys 2019]FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
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
AFN [AAAI 2020]Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions
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

DisscussionGroup & Related Projects

公众号：浅梦学习笔记 微信：deepctrbot 学习小组 加入 主题集合
公众号 微信 学习小组

Main Contributors(welcome to join us!)

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​ Shen Weichen ​

Alibaba Group

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​ Zan Shuxun

Alibaba Group

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Wang Ze ​

Meituan

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Zhang Wutong

Tencent

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​ Zhang Yuefeng

Peking University

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​ Huo Junyi

University of Southampton

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​ Zeng Kai ​

SenseTime

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​ Chen K ​

NetEase

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​ Cheng Weiyu ​

Shanghai Jiao Tong University

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​ Tang

Tongji University

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