Facial Recognition Based Product Recommendation System Using Past Purchases

This study proposes a Facial Recognition-Based Product Recommendation System that enriches e-commerce through the use of facial characteristics and purchase history. With a new K-Nearest Neighbors (KNN) algorithm, the system examines user emotions and buying patterns to make real-time personalized recommendations. Through the combination of computer vision and deep learning, it does away with manual input, providing a hassle-free and interactive shopping experience. The system constantly learns from user tastes, providing dynamic and emotionally intelligent recommendations. This technology represents a major leap forward in personalized retail, opening the door to AI-powered shopping that maximizes customer interaction and satisfaction, ultimately defining the future of user-focused e-commerce with smart, adaptive, and emotion-sensing technology.