Personalized Recommendation Algorithm for Cross-Border E-Commerce English Learning Based on Deep Learning and User Behavior Data

Abstract

The rapid growth of Cross-Border Electronic Commerce (CBEC) has increased demand for personalized English-learning product recommendations. However, traditional recommendation systems struggle to capture complex learner interaction behavior and learning preferences. This study proposes a Dynamic Sand Cat Swarm Attention-based LSTM (DSCS-Att LSTM) model for intelligent recommendation. The model is evaluated using the Amazon Books Reviews dataset, with preprocessing including missing-value imputation and min–max normalization. TF-IDF is used for textual feature extraction, while user interactions are modeled sequentially. The framework integrates attention mechanisms to capture key learning patterns and employs Dynamic Sand Cat Swarm optimization for hyperparameter tuning. Experimental results show high performance with 98.87% accuracy, 98.25% F1-score, and 0.998 AUC, outperforming existing models. The proposed framework analyzes collaborative user interactions and behavioral engagement patterns within CBEC learning environments to improve collaborative recommendation quality.