Machine learning applications in digital advertising performance optimization: A systematic literature review
Bibliographic record
Abstract
The explosion of growth in digital advertising, reaching $798.7 billion by 2025 with 72% of companies adopting artificial intelligence, warrants a systematic understanding of how machine learning is transforming this sector. The objective was to systematically analyze the application of machine learning in web advertising campaigns through a comprehensive review of scientific literature (2010-2025). The PRISMA methodology was implemented with five-dimensional quality criteria (0-3 points), selecting 42 excellent articles (13-15 points). The results reveal a dominance of deep learning (66.6%), with Deep Neural Networks (35.7%) and attention models (19.0%) predominating; convergence toward CTR as a universal metric (95.2%); concentration in e-commerce (61.9%), led by Alibaba (14.3%); and data sparsity as a fundamental limitation (59.5%). Significant algorithmic consolidation is found, but critical gaps in fairness (0%), sustainability (0%), and robustness (0%). Implications include the need for methodological diversification, the development of equity-aware frameworks, and expansion into sectors regulated by privacy-preserving techniques.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".