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/whw199833/gbiz_torch/blob/main/example/model/MaskNetModel_test.py
MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask
https://github.com/whw199833/gbiz_torch/blob/main/gbiz_torch/model/masknet.py
MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask
https://github.com/xiaominglalala/Session_based_Recommendation
Deep Session Interest Network for Click-Through Rate Prediction
https://github.com/xiuyu0000/new_papers_codes/tree/main/fibinet
https://github.com/yibeiou/Advanced_Business_Intelligence
https://github.com/yil479/yelp_review
https://github.com/youngch12/Cluster_KGCN
Knowledge Graph Convolutional Networks for Recommender Systems
https://github.com/yrbahn/deep_match_ctr_prediction
Deep Character-Level Click-Through Rate Prediction for Sponsored Search
https://github.com/yusanshi/NewsRecommendation
https://github.com/zaf11/xDeepFM-
xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
https://github.com/zhanafengxiaomi/xDeepFM_
xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
https://github.com/zhongqiangwu960812/AI-RecommenderSystem
https://github.com/zzaebok/KGCN-pytorch
Knowledge Graph Convolutional Networks for Recommender Systems
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.