Abstract:
The rapid growth of programmatic advertising has made click-through rate (CTR) prediction a cornerstone for optimizing digital marketing spend and enhancing user experience. Accurately forecasting which ad impressions will yield clicks is challenged by massive volumes of high‐dimensional, sparse data and extreme class imbalance, where genuine clicks constitute only a fraction of all impressions. Models must also adapt to evolving user behaviors and contextual shifts to remain effective in dynamic online environments. In this work, we present a comprehensive, reproducible pipeline for offline CTR prediction that emphasizes data quality, robustness, and interpretability. Beginning with meticulous data cleaning and feature preparation—including imputation of missing demographics, categorical encoding, and synthetic oversampling of minority click events—we apply stratified cross‐validation to evaluate candidate models reliably. By leveraging ensemble‐based learning with systematic hyperparameter optimization and careful aggregation of performance metrics, our solution achieves a high discrimination ability (AUC ≈ 0.95) and balanced accuracy (≈ 0.87), substantially outperforming simpler baselines. Beyond empirical performance gains, our approach delivers actionable insights through feature‐importance analysis and localized explanation techniques, revealing the dominant role of temporal context, ad placement, and user browsing patterns in driving clicks. These findings offer practical guidance for ad targeting strategies, while our modular framework lays the groundwork for future extensions in online learning, drift adaptation, and end‐to‐end real‐time bidding integration.
