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Click-Through Rate Prediction

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평가표: 20
보존된 평가 기록: 127

Click-through rate prediction is the task of predicting the likelihood that something on a website (such as an advertisement) will be clicked.

<span style="color:grey; opacity: 0.6">( Image credit: Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/WeiyuCheng/AFN-AAAI-20

Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions

https://github.com/YZBM/Tenceng2019_Finals_Rank1st

xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems

https://github.com/YafeiWu/DIEN

Deep Interest Network for Click-Through Rate Prediction

https://github.com/YinzhenWan/recome_wan

FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction

https://github.com/YunjiaXi/Multi-Level-Interaction-Reranking

Deep Interest Network for Click-Through Rate Prediction

https://github.com/ZJJHYM/RippleNet

RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems

https://github.com/abmitra84/recommender_system

Wide & Deep Learning for Recommender Systems

https://github.com/aivolcano/RecSys_tf2

Wide & Deep Learning for Recommender Systems

https://github.com/aj9011/Wide-and-Deep

Wide & Deep Learning for Recommender Systems

https://github.com/aj9011/Wide-and-Deep-Demand-Forecasting

Wide & Deep Learning for Recommender Systems

https://github.com/alibaba/DeepRec/tree/main/modelzoo/masknet

MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask

https://github.com/alibaba/EasyRec/blob/master/docs/source/models/fibinet.md

FiBiNet++: Reducing Model Size by Low Rank Feature Interaction Layer for CTR Prediction

https://github.com/alibaba/EasyRec/blob/master/docs/source/models/masknet.md

MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask

https://github.com/alibaba/EasyRec/blob/master/easy_rec/python/input/criteo_input.py

Correct Normalization Matters: Understanding the Effect of Normalization On Deep Neural Network Models For Click-Through Rate Prediction

https://github.com/alibaba/EasyRec/blob/master/easy_rec/python/model/autoint.py

AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks

https://github.com/alibaba/EasyRec/blob/master/easy_rec/python/model/dcn.py

Deep & Cross Network for Ad Click Predictions

https://github.com/alibaba/EasyRec/blob/master/easy_rec/python/model/deepfm.py

DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

https://github.com/alibaba/EasyRec/blob/master/easy_rec/python/model/esmm.py

Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate

https://github.com/alibaba/EasyRec/blob/master/easy_rec/python/model/rocket_launching.py

Rocket Launching: A Universal and Efficient Framework for Training Well-performing Light Net

https://github.com/alibaba/EasyRec/blob/master/easy_rec/python/model/wide_and_deep.py

Wide & Deep Learning for Recommender Systems

https://github.com/alibaba/TorchEasyRec

Wide & Deep Learning for Recommender Systems

https://github.com/alibaba/easyrec

Behavior Sequence Transformer for E-commerce Recommendation in Alibaba

https://github.com/alililia/ascend_Fat-DeepFFM

FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine

https://github.com/alsoj/Recommenders-movielens

Wide & Deep Learning for Recommender Systems

https://github.com/anonctr/GDCN/blob/main/models/FiBiNet.py

FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction

https://github.com/anonctr/GDCN/blob/main/models/MaskNet.py

MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask

https://github.com/baiyimeng/umc

DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

https://github.com/beiluo-horizon/PytorchCTR/blob/main/models/ContextNet.py

ContextNet: A Click-Through Rate Prediction Framework Using Contextual information to Refine Feature Embedding

https://github.com/beiluo-horizon/PytorchCTR/blob/main/models/FiBiNetPlus.py

FiBiNet++: Reducing Model Size by Low Rank Feature Interaction Layer for CTR Prediction

https://github.com/beiluo-horizon/PytorchCTR/blob/main/models/GateNet.py

GateNet: Gating-Enhanced Deep Network for Click-Through Rate Prediction

https://github.com/beiluo-horizon/PytorchCTR/blob/main/models/MaskNet.py

MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask

https://github.com/bettenW/Tencent2019_Finals_Rank1st

xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems

https://github.com/bruce-willis/FiBiNET

FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction

https://github.com/caesarjuly/reginx/blob/master/trainer/models/dcn.py

Deep & Cross Network for Ad Click Predictions

https://github.com/caesarjuly/reginx/blob/master/trainer/models/final_mlp.py

FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction

https://github.com/caesarjuly/reginx/blob/master/trainer/models/mask_net.py

MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask

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