Cloud-native AI Ecosystems: Advancing real-time personalization in E-commerce customer experiences

This article examines the convergence of advanced artificial intelligence methodologies and cloud-native environments in revolutionizing e-commerce personalization. The article presents a comprehensive framework for implementing dynamic, real-time personalization systems that leverage transformer-based models, reinforcement learning, and adaptive neural networks to process extensive customer interaction datasets instantaneously. The article addresses critical implementation challenges through serverless computing architectures, containerization strategies, and elastic resource provisioning while emphasizing the importance of explainable AI for maintaining transparency and customer trust. The article demonstrates that cloud-native AI deployments significantly enhance the capacity to deliver highly personalized customer experiences at scale, enabling e-commerce platforms to adapt continuously to individual customer behaviors and preferences. The proposed approaches not only improve computational efficiency and reduce latency but also provide sustainable solutions for ethical compliance in an increasingly regulated digital marketplace, establishing a foundation for the next generation of intelligent e-commerce systems.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-30
DOI
https://doi.org/10.5281/zenodo.17760742
Primary Topic
Recommender Systems and Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Cloud-native AI Ecosystems: Advancing real-time personalization in E-commerce customer experiences

Zenodo (CERN European Organization for Nuclear Research)
Recommender Systems and Techniques
article

Cloud-native AI Ecosystems: Advancing real-time personalization in E-commerce customer experiences

article en

Abstract

This article examines the convergence of advanced artificial intelligence methodologies and cloud-native environments in revolutionizing e-commerce personalization. The article presents a comprehensive framework for implementing dynamic, real-time personalization systems that leverage transformer-based models, reinforcement learning, and adaptive neural networks to process extensive customer interaction datasets instantaneously. The article addresses critical implementation challenges through serverless computing architectures, containerization strategies, and elastic resource provisioning while emphasizing the importance of explainable AI for maintaining transparency and customer trust. The article demonstrates that cloud-native AI deployments significantly enhance the capacity to deliver highly personalized customer experiences at scale, enabling e-commerce platforms to adapt continuously to individual customer behaviors and preferences. The proposed approaches not only improve computational efficiency and reduce latency but also provide sustainable solutions for ethical compliance in an increasingly regulated digital marketplace, establishing a foundation for the next generation of intelligent e-commerce systems.

Zenodo (CERN European Organization for Nuclear Research)
Sri Venkateswara University (IN)
Responsible consumption and production
Openalex Percentile: Top 30%
Recommender Systems and Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.