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by IQAC KIITKIIT Deemed to be University — Internal Quality Assurance Cell

Publication2026

Reliable Digital Marketing Through Click Through Rate Prediction Using XGBoost Model

The record

Type
Publication
Date
1 January 2026
Level
National
Journal
Lecture Notes in Networks and Systems
Volume
1774 LNNS
Pages
425-434
Published
1 January 2026
DOI
10.1007/978-3-032-14186-6_40
Scopus ID
105035622368
ISSN
23673370
Authors
6
Open access
No
Citations
0
International co-authorship
No
Co-author countries
IN

Classification

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UN Sustainable Development Goals

  1. 9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending

    TargetPrimaryModerate confidenceJudged

    Machine learning research using XGBoost for click-through rate prediction in advertising systems, advancing ML capability in data-driven marketing.

    Moderate confidence
    The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
    Judged
    Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
    Produced by
    claude-haiku-4-5-20251001, under kortex-publication-v1

Viksit Bharat @2047

  1. vb-2.3 Services and the knowledge economy

    Sub-themePrimaryModerate confidenceJudged

    Data-driven advertising optimization supports knowledge economy and digital commerce efficiency in service sector.

    Produced by
    claude-haiku-4-5-20251001, under kortex-publication-v1

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KIIT Deemed to be University. “Reliable Digital Marketing Through Click Through Rate Prediction Using XGBoost Model”. Kortex.

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Reliable Digital Marketing Through Click Through Rate Prediction Using XGBoost Model · Kortex