International Journal Of Imaging

Abstract

Personalized AD Targeting System Using Machine Learning for Optimizing AD Delivery

Author: Abhinav gattu,Pranav Raj,B. Hari priya,Mr. K.Naveen chakravarthi

Abstract:

The increasing reliance on digital platforms for marketing and advertising has made personalized ad delivery a critical component of modern business strategies. Traditional advertising systems often fail to adapt to user preferences dynamically, leading to suboptimal engagement and conversion rates. This aims to address these shortcomings by leveraging advanced machine learning techniques to enhance ad targeting and delivery. The system utilizes datasets comprising user demographics, browsing behavior, device information, and temporal patterns to predict the likelihood of ad clicks. Data preprocessing techniques, including handling missing values, encoding categorical data, and balancing imbalanced datasets using SMOTE, ensure data quality and enhance predictive accuracy. Various machine learning models, such as Gradient Boosting Classifier (GBC), Decision Tree Classifier (DTC), and a hybrid Feed-Forward Neural Network (FFNN) with Random Forest (RF), are employed to evaluate the