AI-Driven URL Classification for Targeted Advertising in the Digital Era

Truong Cong Doan (), Bui Khanh Linh (), Ta Khanh Linh (), Nguyen Quynh Trang () and Nguyen Thi Kim Oanh ()
Additional contact information
Truong Cong Doan: Vietnam National University
Bui Khanh Linh: Vietnam National University
Ta Khanh Linh: Vietnam National University
Nguyen Quynh Trang: Vietnam National University
Nguyen Thi Kim Oanh: Vietnam National University

A chapter in New Perspectives and Paradigms in Applied Economics and Business, 2025, pp 181-194 from Springer

Abstract: Abstract In the digital era, effective advertising is based on delivering appropriate content to an adequate audience at the ideal moment. With a wealth of online material, advertisers face the challenge of ensuring their ads resonate with their target audience. Motivated by the successful implementation of recommendation systems on online platforms, our research seeks to utilize AI-based technologies to classify website topics based on its URLs and target advertisements relevant to that topic. We conducted a series of experiments on the training dataset, employing various predictive models to generate predictions. To enhance the accuracy of these predictions, we implemented optimization techniques such as hyperband and ensemble learning. Additionally, we created a website using Streamlit to visualize the experimental results, showcasing the capability of our research to target advertisements based on input URLs. The findings demonstrate the efficiency and accuracy of our predictions, particularly with larger datasets, surpassing the results of previous studies. Consequently, advertisers can deliver more engaging advertisements, increase profits, and improve the user experience in the Vietnamese market.

Keywords: AI-driven advertising; URL classification; Deep learning for marketing; Ensemble learning techniques; Hyperparameter tuning (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-031-77363-1_13

Ordering information: This item can be ordered from
http://www.springer.com/9783031773631

DOI: 10.1007/978-3-031-77363-1_13

Access Statistics for this chapter

More chapters in Springer Proceedings in Business and Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().