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Click-Through Rate Prediction

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하위 태스크: 0
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/xue-pai/FuxiCTR

공식 코드

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

https://github.com/yansuoyuli/reform

공식 코드

RE-SORT: Removing Spurious Correlation in Multilevel Interaction for CTR Prediction

https://github.com/yanyachen/xdeepint

공식 코드

xDeepInt: a hybrid architecture for modeling the vector-wise and bit-wise feature interactions

https://github.com/yuhao318/atomic_feature_mimicking

공식 코드

Unified Low-rank Compression Framework for Click-through Rate Prediction

https://github.com/yushenli807/www24-rat

공식 코드

RAT: Retrieval-Augmented Transformer for Click-Through Rate Prediction

https://github.com/z-y-zhang/one_epoch_phenomenon

공식 코드

Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Prediction Models

https://github.com/zhengrongqin/c2-rec

공식 코드

CT4Rec: Simple yet Effective Consistency Training for Sequential Recommendation

https://github.com/zhishan01/FINT

공식 코드

FINT: Field-aware INTeraction Neural Network For CTR Prediction

https://github.com/zhougr1993/DeepInterestNetwork

공식 코드

Deep Interest Network for Click-Through Rate Prediction

https://github.com/zhougr1993/Rocket-Launching

공식 코드

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

https://github.com/zhouxy1003/tin

공식 코드

Temporal Interest Network for User Response Prediction

https://github.com/zhuchenxv/AutoFIS

공식 코드

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

https://github.com/zhuchenxv/aim

공식 코드

AIM: Automatic Interaction Machine for Click-Through Rate Prediction

https://github.com/zyang1580/dil

공식 코드

Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation

https://github.com/zyouyang/ImpLipReg_CTR

공식 코드

Scaled Supervision is an Implicit Lipschitz Regularizer

https://git.openi.org.cn/OpenModelZoo/FAT-deepFFM

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

https://github.com/1hb6s7t/SmeLU-ms

Real World Large Scale Recommendation Systems Reproducibility and Smooth Activations

https://github.com/2023-MindSpore-1/ms-code-14/tree/main/DeepFM

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

https://github.com/2023-MindSpore-1/ms-code-14/tree/main/Wide_and_Deep

Wide & Deep Learning for Recommender Systems

https://github.com/2023-MindSpore-1/ms-code-14/tree/main/Wide_and_Deep_Multitable

Wide & Deep Learning for Recommender Systems

https://github.com/2023-MindSpore-1/ms-code-220/tree/main/Fat-DeepFFM

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

https://github.com/2023-MindSpore-1/ms-code-220/tree/main/deep_and_cross

Deep & Cross Network for Ad Click Predictions

https://github.com/2023-MindSpore-1/ms-code-220/tree/main/fibinet

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

https://github.com/810410738/DKN

DKN: Deep Knowledge-Aware Network for News Recommendation

https://github.com/Andy1314Chen/FAT-DeepFFM-Paddle

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

https://github.com/Aqiang520/FuxiCTR/blob/main/fuxictr/pytorch/models/MaskNet.py

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

https://github.com/ChristophReich1996/SmeLU

Real World Large Scale Recommendation Systems Reproducibility and Smooth Activations

https://github.com/ChristophReich1996/SmeLU-Triton

Real World Large Scale Recommendation Systems Reproducibility and Smooth Activations

https://github.com/DSXiangLi/CTR

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

https://github.com/DaPenggg/AutoInt

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

https://github.com/DataCanvasIO/DeepTables

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

https://github.com/DeepRec-AI/DeepRec/blob/main/modelzoo/masknet/train.py

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

https://github.com/DownyPrio/xDeepFM

xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems

https://github.com/GitHub-HongweiZhang/prediction-flow

Wide & Deep Learning for Recommender Systems

https://github.com/HaSai666/rec_pangu

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

https://github.com/Hank-Kuo/RippleNet

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

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