Abstract
Abstract Click-through rate (CTR) prediction has become a crucial task in online advertising and other fields. Many researchers focus on improving CTR prediction models by exploring feature interactions. One popular model, Deep Factorization Machine (DeepFM), addresses both high-order and low-order feature interactions, but it overlooks the variability of feature representation in different contexts and lacks a comprehensive explanation of high-order feature interactions. In this paper, we propose a CTR prediction model called DeepFM-GA, which is based on improved feature refinement generation and attention enhancement representation. Firstly, we incorporate an attention convolutional generation module into $\text{DeepFM}_{\text{FRNet}}$, which enriches the feature space by generating complementary features through convolutional neural networks while maintaining context-aware feature representation. Secondly, we utilize a multi-head self-attention layer for feature-enhanced representation, enhancing the model’s ability to select important features. Finally, experiments are conducted on four real-world datasets, and the results show that DeepFM-GA has a better performance compared to other mainstream CTR models.
Published Version
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