User Categorization for Targeted Advertising Using Deep Learning

Targeted advertising plays a crucial role in optimizing marketing campaigns by delivering personalized and relevant advertisements to individual users. User categorization is a fundamental aspect of targeted advertising, involving the classification of users into distinct target groups based on their unique attributes and behaviors. In this abstract, we propose a novel approach that leverages the capabilities of Feedforward Neural Networks (FNNs) for accurate and effective user categorization. Our methodology capitalizes on the advanced features of FNNs to capture intricate patterns and relationships within user data. The architecture of the FNN consists of an input layer that receives a comprehensive range of user attributes, hidden layers responsible for extracting and representing meaningful features, and an output layer that predicts the specific target category for each user, enabling precise and targeted advertising. To train the FNN model, we employ robust optimization algorithms, such as backpropagation with gradient descent, which minimize prediction errors and significantly improve the accuracy of user categorization.