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February 9, 2024

Ad Click-Through Rate Prediction: A Comparative Study of Machine Learning Models

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Authors

APAmiya Ranjan PandaSRSthitapragyan RoutMNMalvika Narsipuram

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Overview

Comparative study demonstrates effective ad click-through rate prediction in internet users, highlighting the importance of algorithm selection for optimizing marketing campaigns.

Key Points

  • XGBoost outperformed competing classification models in predicting ad click-through rates, achieving superior predictive precision across user demographic features.
  • A maximum accuracy of 0.7885 and an AUC-ROC of 0.8778 established the top performance of XGBoost among the evaluated classification algorithms.
  • Comparative study of nine classification models underscores the necessity of algorithm selection, aiding marketers in optimizing online promotional campaigns.

Cite This Study

Panda et al. (2024) studied this question.

synapsesocial.com/papers/68e7b277b6db64358770cac4https://doi.org/10.1109/esic60604.2024.10481562
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Improving CTR Prediction in Advertising with XGBoost2024 · 5 citations
  2. 2TOWARDS CLICK-THROUGH RATE PREDICTION IN ONLINE ADVERTISING2024 · 1 citations
  3. 3A Review of Click-Through Rate Prediction Using Deep Learning2025 · 8 citations
  4. 4Ad-Click Prediction Enhanced by Nonlinear Dynamics-Inspired Feature Extraction and Ensemble Optimization2026
  5. 5Ads Click-Through Rate prediction using Attention based LSTM Mechanism2024 · 3 citations