Collaborative Filtering vs. Content-Based Filtering : A Machine Learning Perspective in Recommendation Systems
Year: 2025
Type: article
Abstract: 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.
Authors: Neelima Jain, Dr. Abid Hussain
Institution: Career Point University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4412059316