Click Through Rate Prediction On Movielens 1M

AUC

Accuracy

Results

Performance results of various models on this benchmark

			Paper Title
STEC	0.9712	-	STEC: See-Through Transformer-based Encoder for CTR Prediction
KNI	0.9449	-	An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation
RippleNet	0.921	84.4	RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
MKR	0.917	84.3	Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation
DCNv3	0.9074	-	FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate Prediction
AutoInt	0.846	-	AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks

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