Click Through Rate Prediction On Bing News

Metrics

AUC

Log Loss

Results

Performance results of various models on this benchmark

			Paper Title
xDeepFM	0.84	0.2649	xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
Wide & Deep	0.8377	0.2668	Wide & Deep Learning for Recommender Systems
DeepFM	0.8376	0.2671	DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
PNN	0.8321	0.2775	Product-based Neural Networks for User Response Prediction
RippleNet	0.678	-	RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
DKN	0.659	-	DKN: Deep Knowledge-Aware Network for News Recommendation
DNN	0.03	0.3382	xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems

0 of 7 row(s) selected.

Click Through Rate Prediction On Bing News

Metrics

AUC

Log Loss

Results

Performance results of various models on this benchmark

			Paper Title
xDeepFM	0.84	0.2649	xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
Wide & Deep	0.8377	0.2668	Wide & Deep Learning for Recommender Systems
DeepFM	0.8376	0.2671	DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
PNN	0.8321	0.2775	Product-based Neural Networks for User Response Prediction
RippleNet	0.678	-	RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
DKN	0.659	-	DKN: Deep Knowledge-Aware Network for News Recommendation
DNN	0.03	0.3382	xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems

0 of 7 row(s) selected.

Click Through Rate Prediction On Bing News | SOTA | HyperAI