Collaborative Filtering vs. Content-Based Filtering : A Machine Learning Perspective in Recommendation Systems

Recommendation systems have become fundamental components of modern digital platforms, powering personalized experiences across e-commerce, entertainment, and social media. This review paper provides a comprehensive analysis of the two primary recommendation approaches: collaborative filtering and content-based filtering, examined through a machine learning lens. We investigate their underlying algorithms, performance characteristics, applications, and emerging trends including deep learning implementations. Our analysis reveals that while collaborative filtering excels in discovering unexpected user preferences and achieving high diversity, content-based filtering offers superior transparency and better handling of new items. The convergence of these approaches through hybrid systems and deep learning architectures represents the current state-of-the-art, addressing individual limitations while leveraging complementary strengths.