Attention-Driven Interaction Network for E-Commerce Recommendations

E-commerce platforms use personalized recommendation systems to improve user experience and increase sales. Traditional models have difficulty capturing the complex relationships between user behavior and product features. This paper introduces the Enhanced Attention and Interaction Network (EAIN), a new approach that combines higher-order feature interactions and attention mechanisms. EAIN includes modules like the Dynamic Interest Network (DIN), Selective Feature Interaction (MaskBlock), and Position-Aware Interaction (PAIM), using data preprocessing and feature engineering to improve user-product relationships. Experimental results show that EAIN performs better than traditional models, especially due to the attention mechanism and higher-order feature interactions. This work contributes to personalized recommendation systems in e-commerce.