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Click-Through Rate Prediction

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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/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Recommendation/WideAndDeep

Wide & Deep Learning for Recommender Systems

https://github.com/Oneflow-Inc/models/tree/main/RecommenderSystems/deepfm

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

https://github.com/PaddlePaddle/PaddleRec

FLEN: Leveraging Field for Scalable CTR Prediction

https://github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/autofis

AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction

https://github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/autoint

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

https://github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/bst

Behavior Sequence Transformer for E-commerce Recommendation in Alibaba

https://github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/dcn

Deep & Cross Network for Ad Click Predictions

https://github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/dcn_v2

DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems

https://github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/deepfefm

Field-Embedded Factorization Machines for Click-through rate prediction

https://github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/deepfm

DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Prediction

https://github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/dien

Deep Interest Evolution Network for Click-Through Rate Prediction

https://github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/din

Deep Interest Network for Click-Through Rate Prediction

https://github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/dpin

Deep Position-wise Interaction Network for CTR Prediction

https://github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/dsin

Deep Session Interest Network for Click-Through Rate Prediction

https://github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/fat_deepffm

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

https://github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/gatenet

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

https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/fgcnn

Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction

https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/fibinet/

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

https://github.com/PaddlePaddle/PaddleRec/tree/release/2.1.0/models/multitask/esmm

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

https://github.com/PaddlePaddle/PaddleRec/tree/release/2.1.0/models/rank/deepfm

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

https://github.com/PaddlePaddle/PaddleRec/tree/release/2.1.0/models/rank/wide_deep

Wide & Deep Learning for Recommender Systems

https://github.com/PaddlePaddle/PaddleRec/tree/release/2.1.0/models/rank/xdeepfm

xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems

https://github.com/Prayforhanluo/CTR_Algorithm

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

https://github.com/QunBB/DeepLearning/blob/main/Recommendation/RANK/fibinet.py

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

https://github.com/QunBB/DeepLearning/blob/main/recommendation/rank/contextnet.py

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

https://github.com/QunBB/DeepLearning/blob/main/recommendation/rank/masknet.py

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

https://github.com/RUCAIBox/EulerNet/blob/main/Model/Baselines/FiBiNet.py

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

https://github.com/Rearcher/ICME2019_Short_Video_Understanding_Challenge_Rank14

xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems

https://github.com/RunlongYu/CELS

https://github.com/Snail110/recsys

Deep & Cross Network for Ad Click Predictions

https://github.com/StephenBo-China/DIEN

Deep Interest Evolution Network for Click-Through Rate Prediction

https://github.com/StephenBo-China/DIEN-DIN

Deep Interest Network for Click-Through Rate Prediction

https://github.com/StephenBo-China/recommendation_system_sort_model

Deep Interest Network for Click-Through Rate Prediction

https://github.com/UlionTse/mlgb

Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction

https://github.com/UlionTse/mlgb/blob/main/mlgb/torch/models/ranking.py

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

https://github.com/WayneDW/DeepLight_Deep-Lightweight-Feature-Interactions

DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving

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