GMINN: Gate-enhanced multi-space interaction neural networks for click-through rate prediction

Abstract

Click-through rate (CTR) prediction is a pivotal challenge in recommendation systems. Existing models are prone to disturbances from noise and redundant features, hindering their ability to fully capture implicit and higher-order feature interactions present in sparse feature data. Moreover, conventional dual-tower models overlook the significance of layer-level feature interactions. To address these limitations, this article introduces Gate-enhanced Multi-space Interactive Neural Networks (GMINN), a novel model for CTR prediction. GMINN adopts a dual-tower architecture in which a multi-space interaction layer is introduced after each layer in the dual-tower deep neural network. This layer allocates features into multiple subspaces and employs matrix multiplication to establish layer-level interactions between the dual towers. Simultaneously, a field-aware gate mechanism is proposed to extract crucial latent information from the original features. Experimental validation on publicly available datasets, Criteo and Avazu, demonstrates the superiority of the proposed GMINN model. Comparative analyses against baseline models reveal that GMINN substantially improves up to 4.09% in AUC and a maximum reduction of 7.21% in Logloss. Additionally, ablation experiments provide further validation of the effectiveness of GMINN.