Revolutionizing Web Browsing with AI-Powered Personalized Ad Blockers

Abstract

The growth of obtrusive and unrelated online advertisements has profoundly affected user experience and privacy. Traditional ad blockers, based on static rule-based approaches, find it difficult to cope with changing advertising tactics. This research suggests an AI-based personalized ad blocker aimed at transforming web browsing by providing dynamic, smart, and user-centric ad filtering. Based on machine learning models and behavioral analytics, the solution is tailored to match individual user behavior while ensuring high accuracy in recognizing and removing intrusive ads. The system combines computer vision and natural language processing (NLP) methods for end-to-end ad content analysis, enabling real-time detection of visual and textual components. Privacy is prioritized through on-device processing and federated learning approaches with minimal data exposure. Performance evaluations demonstrate significant improvements in detection accuracy, personalization, and browsing speed relative to conventional methods. This research highlights the promise of AI-driven solutions to improve user experience, maintain privacy, and revolutionize digital content consumption.