Vision based artificial intelligence for optimizing e-commerce experiences in virtual reality

Abstract

Advancements in artificial intelligence (AI) and digital technologies have deeply reshaped consumer behavior and marketing strategies, demanding innovative approaches to decoding and optimizing customer engagement. This dissertation explores the potential of vision deep neural networks, generative AI, and virtual reality (VR) to analyze emotional and behavioral responses and enhance strategic business insights in digital commerce. This research focuses on convolutional neural network (CNN) architectures and evaluates their effectiveness in predicting consumer engagement through facial emotion recognition (FER). The dissertation addresses limitations in FER datasets by integrating synthetic data generated using generative adversarial networks (GANs) and real-world open data extracted from social media. This hybrid approach enhances model generalizability across diverse demographics and advertisement categories. The dissertation further investigates the role of immersive VR environments in influencing consumer engagement and purchase intent. By leveraging multi-modal causal analysis, it examines the interplay between VR design complexity, exposure sequencing, and emotional responses, providing actionable insights for optimizing e-commerce experiences. Ethical considerations are central to this research, which address biases, privacy concerns, and transparency in AI-driven decision-making. The findings contribute to the development of robust, inclusive, and scalable frameworks for personalized commerce, offering a transformative approach to understanding consumer behavior in digital environments. Through a systematic integration of vision deep learning, generative AI, and VR technologies, this dissertation bridges critical gaps in systems engineering research and business applications; advancing both theoretical understandings and practical applications in consumer engagement optimization.