HYPER-SCALABLE CLOUD-NATIVE AI FRAMEWORKS FOR PREDICTIVE HYPER-PERSONALIZATION IN HIGH-TRAFFIC E-COMMERCE ECOSYSTEMS

Abstract

This paper proposes a hyper-scalable, cloud-native AI framework designed to deliver predictive hyper-personalization in high-traffic e-commerce ecosystems. The framework leverages a microservices-based architecture, containerization, and dynamic load balancing to scale efficiently and manage real-time data inflows, enabling precise and personalized user experiences. By incorporating a robust data ingestion layer, a feature engineering pipeline, and a combination of collaborative filtering and deep learning models, the framework achieves high accuracy and low latency in recommendations. Experimental evaluations demonstrate the framework’s ability to maintain personalization depth and accuracy across various traffic loads, though minor latency increases are observed under extreme conditions. Comparisons with traditional systems highlight the advantages of the cloud-native approach in managing scalability and performance. These findings underscore the potential of cloud-native AI frameworks in enhancing user engagement and satisfaction in dynamic e-commerce environments. Future optimizations to improve response time and resource utilization under peak load conditions are also discussed.