A Personalized Product Recommendation System for E-Commerce Platforms Based on Artificial Intelligence and Image Processing Technologies

With the rapid development of e-commerce, the variety of products available on platforms is increasing, and consumers' demand for personalized and accurate recommendations is growing stronger. Product recommendation systems based on Artificial Intelligence (AI) and image processing technologies have gained widespread attention as a critical means to enhance user experience and sales conversion rates on e-commerce platforms. Existing recommendation systems typically rely on user behavior data or product attributes; however, these methods often overlook the visual features and multi-label information of products, limiting the accuracy and diversity of recommendations. Furthermore, a single recommendation mechanism fails to meet the complex needs of users, resulting in unsatisfactory recommendation outcomes. To address this issue, this paper proposes a product recommendation system based on AI and image processing technologies. The first part of the research focuses on multi-label classification of products, utilizing image recognition and text analysis techniques to extract visual features and textual information, enabling more accurate and detailed label classification. The second part explores a personalized recommendation approach based on similarity retrieval, combining user behavior data and product features to provide personalized recommendations through similarity calculation. Experimental results show that the proposed method significantly improves the accuracy of product recommendations and user satisfaction, demonstrating strong application potential.