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Title: GitHub - codeants2012/DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR models.

Open Graph Title: GitHub - codeants2012/DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR models.

X Title: GitHub - codeants2012/DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR models.

Description: Easy-to-use,Modular and Extendible package of deep-learning based CTR models. - codeants2012/DeepCTR

Open Graph Description: Easy-to-use,Modular and Extendible package of deep-learning based CTR models. - codeants2012/DeepCTR

X Description: Easy-to-use,Modular and Extendible package of deep-learning based CTR models. - codeants2012/DeepCTR

Opengraph URL: https://github.com/codeants2012/DeepCTR

X: @github

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A Convolutional Click Prediction Modelhttp://ir.ia.ac.cn/bitstream/173211/12337/1/A%20Convolutional%20Click%20Prediction%20Model.pdf
Deep Learning over Multi-field Categorical Data: A Case Study on User Response Predictionhttps://arxiv.org/pdf/1601.02376.pdf
Product-based neural networks for user response predictionhttps://arxiv.org/pdf/1611.00144.pdf
Wide & Deep Learning for Recommender Systemshttps://arxiv.org/pdf/1606.07792.pdf
DeepFM: A Factorization-Machine based Neural Network for CTR Predictionhttp://www.ijcai.org/proceedings/2017/0239.pdf
Learning Piece-wise Linear Models from Large Scale Data for Ad Click Predictionhttps://arxiv.org/abs/1704.05194
Deep & Cross Network for Ad Click Predictionshttps://arxiv.org/abs/1708.05123
Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networkshttp://www.ijcai.org/proceedings/2017/435
Neural Factorization Machines for Sparse Predictive Analyticshttps://arxiv.org/pdf/1708.05027.pdf
xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systemshttps://arxiv.org/pdf/1803.05170.pdf
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networkshttps://arxiv.org/abs/1810.11921
Deep Interest Network for Click-Through Rate Predictionhttps://arxiv.org/pdf/1706.06978.pdf
Deep Interest Evolution Network for Click-Through Rate Predictionhttps://arxiv.org/pdf/1809.03672.pdf
Operation-aware Neural Networks for User Response Predictionhttps://arxiv.org/pdf/1904.12579.pdf
Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction https://arxiv.org/pdf/1904.04447
Deep Session Interest Network for Click-Through Rate Prediction https://arxiv.org/abs/1905.06482
FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Predictionhttps://arxiv.org/pdf/1905.09433.pdf
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