Click-through rate prediction model integrating user interest and multi-head attention mechanism

Journal of Big Data

Table 2 Effectiveness comparison of different algorithms

Model	Criteo		Movielens-1M		Avazu
	AUC	LOSS	AUC	LOSS	AUC	LOSS
FM	0.6869	0.5286	0.5347	0.4462	0.5437	0.6221
Weidedeep	0.7066	0.4827	0.8328	0.3334	0.7424	0.4117
Deepfm	0.7283	0.4707	0.8340	0.3346	0.7461	0.4041
AFM	0.7220	0.4754	0.8295	0.3358	0.7567	0.4012
DCN	0.7094	0.4920	0.8249	0.3393	0.7349	0.4139
NFM	0.7027	0.5645	0.8297	0.3357	0.7400	0.4179
PNN	0.7084	0.4870	0.8312	0.3353	0.7374	0.4096
Autoint	0.7060	0.6049	0.8362	0.3393	0.7450	0.4496
Deepcrosing	0.7375	0.4732	0.8373	0.3305	0.7572	0.3989
IARM	0.7545	0.4830	0.8386	0.3296	0.7652	0.3994