This article examines the transformative impact of AI-powered recommendation engines on the eCommerce landscape. It explores how these sophisticated systems have evolved from basic collaborative filtering mechanisms to complex architectures leveraging deep learning, reinforcement learning, and contextual understanding. The technical foundations of modern recommendation systems are analyzed, including collaborative filtering, content-based approaches, neural network architectures, and hybrid methodologies that address the inherent limitations of individual techniques. The article delves into real-time data processing infrastructure, highlighting the critical components that enable millisecond-level personalization at scale. Additionally, it investigates how contextual factors—including temporal dynamics, sequential patterns, situational context, and session-based information—enhance recommendation relevance. The article further examines evaluation frameworks and optimization techniques essential for continuous system improvement. Ethical considerations surrounding transparency, privacy, and fairness receive significant attention alongside emerging trends that point toward more immersive and emotionally intelligent recommendation experiences through generative AI, affective computing, and augmented reality integration. Throughout, the economic and experiential benefits of effective recommendation implementations are emphasized as critical competitive differentiators in contemporary digital commerce.
Keywords: Personalization, collaborative filtering, contextual recommendation, hybrid recommendation systems, machine learning
