https://github.com/Havlong/masknet-api/tree/main/dnn-python/masknet
MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask
Papers with Code 아카이브
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/Hirosora/LightCTR
https://github.com/JShubhM15/recommender_systems
Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems
https://github.com/Jessinra/GDP-RippleNet
RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
https://github.com/Jessinra/GDP-RippleNet-Ori
RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
https://github.com/JiangShaoYin/FIGNN
Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction
https://github.com/JianzhouZhan/Awesome-RecSystem-Models
https://github.com/Jyonn/Legommenders/blob/master/model/predictors/mask_net_predictor.py
MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask
https://github.com/KanchiShimono/KGCN
Knowledge Graph Convolutional Networks for Recommender Systems
https://github.com/Ki-Seki/KGCN-pytorch-updated
Knowledge Graph Convolutional Networks for Recommender Systems
https://github.com/Leavingseason/OpenLearning4DeepRecsys
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
https://github.com/LeeHyeJin91/Wide_and_Deep
https://github.com/Li-fAngyU/DSIN_paddle
Deep Session Interest Network for Click-Through Rate Prediction
https://github.com/LinJayan/DCN_V2_Paddle
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
https://github.com/LinJayan/FAT_DeepFFM_Paddle
FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine
https://github.com/LinJayan/FLEN-Paddle
https://github.com/LionQYW/Dien----Deep-Interest-Evolution-Network-for-Click-Through-Rate-Prediction
Deep Interest Evolution Network for Click-Through Rate Prediction
https://github.com/Mind23-2/MindCode-45
FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine
https://github.com/MindSpore-paper-code-2/code399/tree/main/DeepFM
DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Prediction
https://github.com/MindSpore-paper-code-2/code399/tree/main/Wide_and_Deep
DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Prediction
https://github.com/MindSpore-scientific-2/code-10/tree/main/SmeLU-master
Real World Large Scale Recommendation Systems Reproducibility and Smooth Activations
https://github.com/MindSpore-scientific-2/code-11/tree/main/ShortFuse-Biomedical-Time-Series
Hybrid CNN Based Attention with Category Prior for User Image Behavior Modeling
https://github.com/MindSpore-scientific-2/code-3/tree/main/wide-minima-density-hypothesis
https://github.com/MindSpore-scientific-2/code-4/tree/main/Automatic-Feature-Interaction
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
https://github.com/MindSpore-scientific-2/code-4/tree/main/PR_Product
Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data
https://github.com/MindSpore-scientific-2/code-5/tree/main/Automatic-Feature-Interaction
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
https://github.com/MindSpore-scientific-2/code-9/tree/main/Automatic-Feature-Interaction
https://github.com/MindSpore-scientific-2/code-9/tree/main/Block_Model
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
https://github.com/MindSpore-scientific/code-1/tree/main/ShortFuse-Biomedical-Time-Series
Hybrid CNN Based Attention with Category Prior for User Image Behavior Modeling
https://github.com/MindSpore-scientific/code-1/tree/main/SmeLU-master
Real World Large Scale Recommendation Systems Reproducibility and Smooth Activations
https://github.com/MindSpore-scientific/code-11/tree/main/SmeLU-master
Real World Large Scale Recommendation Systems Reproducibility and Smooth Activations
https://github.com/MindSpore-scientific/code-12/tree/main/Alexnet-ABeffect
https://github.com/MindSpore-scientific/code-13/tree/main/PR_Product
Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data
https://github.com/MindSpore-scientific/code-13/tree/main/SmeLU-master
Real World Large Scale Recommendation Systems Reproducibility and Smooth Activations
https://github.com/MindSpore-scientific/code-4/tree/main/Automatic-Feature-Interaction
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
https://github.com/MindSpore-scientific/code-4/tree/main/Block_Model
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.