Edge AI for Ultra-Fast Personalized Recommendation Systems in E-Commerce

This paper presents an innovative Edge AI-based recommendation system designed to deliver ultra-fast and highly personalized user experiences in e-commerce environments. Traditional cloud-based recommendation systems often suffer from latency and privacy issues due to centralized data processing. To address these limitations, the proposed framework leverages Edge AI and Federated Learning (FL) to enable real-time, on-device recommendation generation while preserving user privacy. The system dynamically adapts to individual preferences through contextual data such as location, browsing behavior, and temporal factors. Experimental evaluation demonstrates a 30% reduction in latency, 20% higher contextual accuracy, and 95% data privacy efficiency compared to existing hybrid and cloud models. These results highlight the model's superior adaptability, privacy compliance, and scalability, establishing it as a practical and high-performance solution for next-generation personalized e-commerce systems.

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