https://github.com/caesarjuly/reginx/blob/master/trainer/models/xdeepfm.py
xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
Papers with Code 아카이브
Click-Through Rate Prediction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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/chenjiyan2001/paddle-FGCNN
Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction
https://github.com/chenxingqiang/RecSys-CTR-Model-2020/tree/main/models/GateNet
GateNet: Gating-Enhanced Deep Network for Click-Through Rate Prediction
https://github.com/clabrugere/ctr-prediction
Towards Deeper, Lighter and Interpretable Cross Network for CTR Prediction
https://github.com/clabrugere/final-mlp
FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction
https://github.com/codectr/RefineCTR/blob/main/FRCTR/model_zoo/fibinet.py
https://github.com/codectr/RefineCTR/blob/main/FRCTR/module_zoo/contextnet.py
https://github.com/codectr/RefineCTR/blob/main/FRCTR/module_zoo/gatenet.py
GateNet: Gating-Enhanced Deep Network for Click-Through Rate Prediction
https://github.com/codlife/NLP
https://github.com/cpapadimitriou/Click-Through-Rate-prediction
An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy
https://github.com/cripac-dig/graphctr
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
https://github.com/datawhalechina/fun-rec
https://github.com/datawhalechina/torch-rechub/blob/main/torch_rechub/models/ranking/deepffm.py
FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine
https://github.com/datawhalechina/torch-rechub/blob/main/torch_rechub/models/ranking/fibinet.py
https://github.com/ddatta-DAC/Learning
Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction
https://github.com/ddoeunn/recommender-system-implementation
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
https://github.com/deepakshankar94/Exploring-DL-based-Recommendation-Systems
https://github.com/deepakshankar94/Movie-Recommendation-System
https://github.com/ekaksher/FedCTR
FedCTR: Federated Native Ad CTR Prediction with Multi-Platform User Behavior Data
https://github.com/fengtong-xiao/DMBGN
https://github.com/floraxhuang/Movie-Recommendation-System
https://github.com/fuyuanlyu/autofs-in-ctr
LPFS: Learnable Polarizing Feature Selection for Click-Through Rate Prediction
https://github.com/hsientzucheng/MKR.PyTorch
Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation
https://github.com/huangjunheng/recommendation_model
https://github.com/hwwang55/DKN
https://github.com/hzn666/rlbid_ea
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
https://github.com/iFe1er/AlitaNet
https://github.com/igorbrigadir/awesome-twitter-algo/blob/main/masknet.md
MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask
https://github.com/imvishvaraj/ctr_nlp
https://github.com/jamesdvance/predicting_clicks
https://github.com/jccarles/product-nets
Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data
https://github.com/jiean001/models_m/tree/main/DIEN
Deep Interest Evolution Network for Click-Through Rate Prediction
https://github.com/jiwidi/Behavior-Sequence-Transformer-Pytorch
Behavior Sequence Transformer for E-commerce Recommendation in Alibaba
https://github.com/johnlevi/recsys
https://github.com/johnnyjana730/MVIN
RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
https://github.com/jsleroux/Recommender-Systems
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.