AN EXAMINATION OF AI-BASED RECOMMENDATION SYSTEMS IN ENHANCING USER PERSONALIZATION IN DIGITAL PLATFORMS

Abstract

Artificial Intelligence (AI)-based recommendation systems have become an essential component of modern digital platforms, significantly influencing user experience and engagement. This study examines the role of AI-driven recommendation systems in enhancing user personalization across digital platforms such as e-commerce websites, streaming services, social media applications, and online learning environments. The research explores how machine learning algorithms, data analytics, and predictive modeling enable platforms to analyze user behavior, preferences, and interaction patterns to provide personalized recommendations. The study further investigates the impact of these systems on customer satisfaction, decision-making, retention, and platform efficiency. A qualitative and quantitative review of existing literature and case studies highlights that AI-based recommendation systems improve content relevance, increase user engagement, and strengthen consumer trust by delivering tailored experiences. However, the study also identifies challenges related to data privacy, algorithmic bias, and ethical concerns in the collection and use of user data. The findings suggest that while AI-powered personalization offers substantial benefits for both users and digital businesses, the effectiveness of recommendation systems depends on transparency, fairness, and responsible data management practices. This study contributes to the growing discourse on AI integration in digital platforms by providing insights into the opportunities and limitations of personalized recommendation technologies in the digital age.

Keywords : Artificial Intelligence, Recommendation Systems, User Personalization, Digital Platforms, Machine Learning, User Experience, Data Analytics, Predictive Modeling, Consumer Engagement, Algorithmic Bias.