Optimizing E-Commerce Platforms with AI-Enabled Visual Search: Assessing User Behavior, Interaction Metrics, and System Accuracy

Abstract

The integration of artificial intelligence (AI) into e-commerce platforms has revolutionized user interaction, with AI-enabled visual search emerging as a transformative tool for enhancing product discovery and customer engagement. This study explores the impact of AI-driven visual search systems on user behavior, interaction metrics, and system performance in digital commerce. Utilizing a mixed-methods approach, the research evaluates system architecture, user satisfaction, accuracy metrics, and ethical considerations through comparative analysis of keyword-based versus image-based search models. Results indicate that visual search significantly improves user satisfaction (by 85%), reduces task completion time (by 38%), and enhances precision and recall metrics across all evaluation parameters. The study also highlights the importance of explainable AI (XAI), multimodal interaction analysis, and cybersecurity frameworks to ensure fairness, transparency, and secure data handling. Strategic recommendations emphasize the adoption of multimodal interfaces, adaptive learning, and ethical AI governance. The findings underscore the pivotal role of visual search in optimizing e-commerce performance and user-centric digital experiences.