Multi-scenario CTR prediction via enhanced scene-aware transformer framework

Abstract In the context of multi-scenario recommendation, multi-scenario click-through-rate (MS-CTR) prediction plays a crucial role in effectively personalizing recommendations on commercial platforms. However, when confronted with multi-scenario data, models often face challenges such as overfitting, inadequate feature representation, and unstable optimization, which hinder the performance and reliability of CTR prediction. To tackle these challenges, this study introduces the enhanced scene-aware transformer (ESAT) framework for MS-CTR prediction. This framework is divided into five modules. First, the structural position-aware scene encoding module converts scene attributes into fixed-dimensional embedding vectors and the scene adaptive transformation module uses a nonlinear transformation to dynamically adjust scene features. The cross-scene regularization module uses multi-sample dropout technology to enhance generalization ability and prevent overfitting. The scene-aware discriminative learning module applies contrastive learning to optimize the similarity between samples. Finally, the hierarchical stability control module introduces ClippyGrad optimizer and $L_\infty $ regularization to accurately control gradient updates, avoid excessive steps, and improve training stability. Experiments conducted on large-scale multi-scenario datasets confirm the effectiveness of the proposed module. The results indicate that the ESAT model substantially elevates performance in MS-CTR prediction tasks, especially in terms of generalization to new scenes, precision in feature representation, and stability in parameter updates.