MACHINE LEARNING IN ADTECH: TRANSFORMING DIGITAL ADVERTISING

This comprehensive article explores the transformative impact of machine learning technologies on digital advertising, focusing on the evolution and implementation of advanced ML systems across the AdTech pipeline. It examines the intricate processes involved in modern advertising technology, from creative content analysis to real-time bidding optimization. The article details how machine learning algorithms revolutionize audience targeting, bid optimization, and campaign performance through sophisticated data processing and decision-making systems. It discusses key technical implementationconsiderations, including model architecture design and system infrastructure requirements for large-scale ML deployments in advertising platforms.Additionally, the article addresses emerging challenges and future directions in the field, particularly concerning privacy regulations, model improvements, and system optimizations.Machine Learning in Adtech:

Authors

Publication Details

Journal
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY
Published
2025-01-16
DOI
https://doi.org/10.34218/ijcet_16_01_048
Citations
4
Primary Topic
Modeling, Simulation, and Optimization
Type
article
Field-Weighted Citation Impact
21.32
Controls
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article

MACHINE LEARNING IN ADTECH: TRANSFORMING DIGITAL ADVERTISING

V.M. Chandrasekaran
4 citations
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY
Modeling, Simulation, and Optimization
21.32
article

MACHINE LEARNING IN ADTECH: TRANSFORMING DIGITAL ADVERTISING

V.M. Chandrasekaran
article en
4 citations

Abstract

This comprehensive article explores the transformative impact of machine learning technologies on digital advertising, focusing on the evolution and implementation of advanced ML systems across the AdTech pipeline. It examines the intricate processes involved in modern advertising technology, from creative content analysis to real-time bidding optimization. The article details how machine learning algorithms revolutionize audience targeting, bid optimization, and campaign performance through sophisticated data processing and decision-making systems. It discusses key technical implementationconsiderations, including model architecture design and system infrastructure requirements for large-scale ML deployments in advertising platforms.Additionally, the article addresses emerging challenges and future directions in the field, particularly concerning privacy regulations, model improvements, and system optimizations.Machine Learning in Adtech:

INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGYVol. 16(1)
Openalex Percentile: Top 1%
Modeling, Simulation, and Optimization
21.32
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