Deep Learning Techniques for Online Information-Seeking User Analysis and Classification

Abstract

Enhancing digital experiences and gaining a fuller picture of people's online activity necessitates classifying internet users according to the information they seek. This investigation investigates the potential of machine learning methodologies to categorize individuals based on their online activity, which encompasses their search queries, website visits, and interactions with the content they encounter. The research employs neural networks, decision trees, and support vector machines to categorize users into three categories: meticulous researchers, goal-driven clients, and occasional visits. The study underscores the necessity of meticulously considering feature selection, data preparation, and model testing in order to obtain reliable results. The results support the notion of personalized content sharing, targeted advertisements, and enhanced methodologies as a means of enhancing user engagement with digital platforms.