Enhancing CTR Prediction with Dynamic Fusion Network

The forecasting of Click-Through Rates (CTR) is crucial in online advertising and recommendation systems, significantly impacting revenue and user experience. Traditional CTR models rely on simple feature representations and static interactions, making it difficult to accurately capture complex user behaviors. Although deep learning has made progress in feature processing, it still faces challenges in adapting to dynamic environments and maintaining model stability. This paper introduces the Dynamic Fusion Prediction Network (DFPN), which innovatively employs a Dynamic Context Encoder (DCE) and FusionGate mechanism to enhance feature representation and interaction. The DCE adapts to varying contexts, allowing for a more personalized representation of features. FusionGate further improves feature importance identification, contributing to enhanced prediction accuracy and robustness. Additionally, dropout and layer normalization are integrated to mitigate overfitting and improve generalization. The DFPN is designed to be orthogonal to existing CTR models such as FM and DeepFM, enabling flexible integration for performance enhancement. Experiments on public benchmarks demonstrate that DFPN significantly improves accuracy, generalization, and stability, confirming its superior performance in dynamic environments.