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/jyonn/legommenders
https://github.com/keyuchen886/GoodReads
https://github.com/kupuSs/DIEN-pipline
Deep Interest Evolution Network for Click-Through Rate Prediction
https://github.com/liangcaisu/stem
BARS-CTR: Open Benchmarking for Click-Through Rate Prediction
https://github.com/lucasthim/auto-fuzzy
https://github.com/luxinming/PaddleRec20220725/tree/master/models/rank/fat_deepffm
FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine
https://github.com/luxinming/PaddleRec20220725/tree/master/models/rank/gatenet
GateNet: Gating-Enhanced Deep Network for Click-Through Rate Prediction
https://github.com/lyhue1991/eat_pytorch_in_20_days
https://github.com/manujosephv/pytorch_tabular
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
https://github.com/massquantity/LibRecommender
https://github.com/meabhishekkumar/iith_session_2020
https://github.com/microsoft/recommenders
https://github.com/mikudehuane/Configurable-Tensorflow-DIN
https://github.com/mindspore-ai/models/tree/master/official/recommend/deep_and_cross
https://github.com/mindspore-ai/models/tree/master/official/recommend/deepfm
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
https://github.com/mindspore-ai/models/tree/master/official/recommend/wide_and_deep
https://github.com/mindspore-ai/models/tree/master/research/recommend/DIEN
Deep Interest Evolution Network for Click-Through Rate Prediction
https://github.com/mindspore-ai/models/tree/master/research/recommend/Fat-DeepFFM
FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine
https://github.com/mindspore-ai/models/tree/master/research/recommend/autodis
An Embedding Learning Framework for Numerical Features in CTR Prediction
https://github.com/mindspore-ai/models/tree/master/research/recommend/fibinet
https://github.com/mostsuperman/KGCN-ML
Knowledge Graph Convolutional Networks for Recommender Systems
https://github.com/mostsuperman/test
Knowledge Graph Convolutional Networks for Recommender Systems
https://github.com/myungkeun-cho/taobao_dlrm
Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems
https://github.com/nebyu08/tiny_recommendation
https://github.com/nihalsangeeth/behaviour-seq-transformer
Behavior Sequence Transformer for E-commerce Recommendation in Alibaba
https://github.com/nikithamalgifb/dlrm
Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems
https://github.com/nvzhou/DeepRec-public/blob/main/modelzoo/masknet/README.md
MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask
https://github.com/objectc/DeepFM
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
https://github.com/p768lwy3/torecsys
FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine
https://github.com/p768lwy3/torecsys/blob/master/torecsys/models/ctr/feature_importance_and_bilinear_feature_interaction_network.py
https://github.com/paper-submission-coder/seminar
https://github.com/paulpig/AD_CTR/blob/main/fuxictr/pytorch/models/MaskNet.py
MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask
https://github.com/pollyyu/Final_Project_MachineLearning_in_TensorFlow_Berkeley
https://github.com/psycho-demon/CELS
https://github.com/pwc-1/Paper-9/tree/main/4/Automatic-Feature-Interaction
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.