A Comprehensive Evaluation of the Strengths and Weaknesses of Different AI Algorithms in Delivering Tailored User Experiences Based on Empirical Evidence

Abstract

Joseph Foley*

This paper presents a comprehensive evaluation of artificial intelligence algorithms utilized to deliver personalized user experiences across digital platforms. This research utilises empirical evidence from recent studies to evaluate the performance, strengths, and limitations of collaborative filtering, content-based filtering, deep learning methods, and hybrid systems. The findings indicate that although AI-driven personalisation enhances user engagement and satisfaction, significant challenges remain, including cold-start problems, algorithmic bias, privacy concerns, scalability limitations, and the emergence of filter bubbles. This study highlights key gaps in existing research and suggests directions for developing more ethical, transparent, and effective personalisation systems.

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