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홈 뉴스 최신 연구 논문 튜토리얼 데이터셋 백과사전 SOTA LLM 모델 GPU 랭킹 컨퍼런스

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Click Through Rate Prediction On Kkbox

평가 지표

AUC

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	AUC	Paper Title	Repository
AutoInt+	0.8534	AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks	-
xDeepFM	0.8535	xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems	-
DCNv3	0.8557	FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate Prediction	-
DeepFM	0.8531	DeepFM: A Factorization-Machine based Neural Network for CTR Prediction	-
DeepIM	0.8537	Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions
DCNv2	0.8531	DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems	-

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Click Through Rate Prediction On Kkbox | SOTA | HyperAI초신경