Smart recommendations in E-commerce using convolutional neural networks and collaborative filtering
Xiuyuan Li · Australian Journal of Electrical & Electronics Engineering · 2025
This study proposes a hybrid recommendation framework for online shopping platforms that integrates Convolutional Neural Networks (CNNs) with collaborative filtering (CF) to improve personalization accuracy. Traditional recommendation approaches, such as content-based and collaborative filtering methods, often suffer from data sparsity, cold-start problems, and limited capability to capture rich item features, which reduces recommendation effectiveness in large-scale e-commerce systems. To overcome these challenges, the proposed model leverages CNNs to extract visual and textual features of products while CF learns latent user–item interaction patterns. By combining content-level representations with hidden user preferences and engagement behavior, the hybrid approach enhances recommendation relevance and accuracy. The model was trained and evaluated using the Kaggle E-Commerce Product Recommendation Collaborative dataset. Experimental results demonstrate superior prediction performance compared to conventional methods, highlighting improved scalability, accuracy, and user engagement. The findings confirm the effectiveness of the CNN-based collaborative filtering framework as a robust and data-driven solution for modern e-commerce recommendation systems, enabling more personalized and relevant shopping experiences.