Usage of Deep learning Techniques for Personalized Recognition Systems in Online Shopping

In recent years, online shopping has become an integral part of our daily lives, offering convenience and accessibility to various products. However, with the overwhelming number of options available, finding the right product that matches individual preferences and needs can take time and effort. To address this challenge, we propose a personalized recognition system for online shopping that leverages the power of deep learning techniques. The personalized recognition system harnesses the power of deep learning to revolutionize online shopping by providing users with a highly customized and satisfying shopping experience. By leveraging advanced computer vision and recommendation systems techniques, we aim to bridge the gap between users and the vast array of products available, ultimately enhancing the efficiency and enjoyment of online shopping. This study introduces a robust monitoring system to observe the buying experience during multiple shop interactions. The system utilizes a combination of refined physiological data and facial expressions to effectively recognize consumers’ emotions while minimizing biases related to age, race, and gender evaluations. To validate its efficiency, the system’s data is compared against the results obtained from conventional video analysis in a pilot study. The findings of this study demonstrate that the proposed approach can significantly contribute to the analysis of consumer experiences in a retail environment.