Journal of Big Data

Table 6 Displays the IARM model's performance on the Criteo dataset under various test sets

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

Model	Criteo
Proportion	0.2		0.3		AVG. changes
	AUC	LOSS	AUC	LOSS	AUC	LOSS
FM	0.6869	0.5286	0.6785	0.5368	− 0.0084	+ 0.0082
Weidedeep	0.7066	0.4827	0.7007	0.4846	− 0.0059	+ 0.0019
Deepfm	0.7283	0.4707	0.7249	0.4792	− 0.0034	+ 0.0085
AFM	0.7220	0.4754	0.7084	0.4877	− 0.0136	+ 0.0123
DCN	0.7094	0.4920	0.7042	0.5010	− 0.0052	+ 0.009
NFM	0.7027	0.5645	0.7005	0.5654	− 0.0022	+ 0.0009
PNN	0.7084	0.4870	0.7013	0.4979	− 0.0071	+ 0.0109
Autoint	0.7060	0.6049	0.6921	0.6552	− 0.0139	+ 0.0503
Deepcrosing	0.7375	0.4732	0.7356	0.4792	−0.0019	+ 0.006
IARM	0.7545	0.4830	0.7527	0.4993	− 0.0018	+ 0.0163

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