Explainable AI for Predicting User Behavior in Digital Advertising

Online advertising has ushered in a new era of digital communication and business transformation. However, the inundation of digital content necessitates a deeper understanding of user behavior to ensure meaningful engagement. This paper investigates the potential of machine learning in predicting and analyzing user behavior in the realm of online advertising. Utilizing a dataset encompassing user interactions with advertisements, we deployed three machine learning models: Random Forest, Logistic Regression, and Gradient Boosting. Our findings highlight that the Random Forest model outperformed with an accuracy of 97.67%, followed closely by Logistic Regression and Gradient Boosting. Furthermore, recognizing the opaque nature of machine learning models, our research leverages SHAP and LIME, tools of explainable AI, ensuring that our models’ decisions remain interpretable. This study under-scores the power of a data-driven approach in online advertising, emphasizing the necessity for both precision and transparency in this digital age.