Intelligent Product Image Recommendation System for E-commerce Platforms Based on Deep Learning

Computer vision and deep learning technologies have become pivotal in addressing the challenges of large-scale e-commerce platforms, particularly in the development of intelligent recommendation systems. Traditional methods, such as collaborative filtering and content-based filtering, often fail to handle the complexity of visual data and suffer from issues like the cold-start problem and data sparsity. To overcome these limitations, this paper proposes a novel product image recommendation system that integrates Convolutional Neural Networks (CNNs) and attention mechanisms to enhance feature extraction and representation. The CNN module hierarchically captures semantic features from product images, while the attention mechanism dynamically focuses on visually significant regions, such as logos, textures, and patterns, thereby improving recommendation precision and personalization. To ensure scalability and efficiency, the system incorporates advanced optimization strategies, including weighted loss functions and adaptive learning rate schedules, which are well-suited for large-scale e-commerce datasets. Experimental results on a publicly available dataset demonstrate the superiority of the proposed model, with significant improvements in key metrics such as Precision, Recall, F1-Score, and Mean Average Precision (MAP), outperforming traditional methods and baseline deep learning models. This study provides a robust and scalable solution for image-based recommendation systems, demonstrating practical value in e-commerce applications by improving user engagement and conversion rates. Future research could explore the integration of multi-modal data and real-time optimization techniques to further enhance recommendation effectiveness in dynamic e-commerce environments.