Collaborative Filtering-Based Personalized Recommendations: Challenges, Limitations, and Applications

Recommendation systems are vital for personalized experiences in today’s digital landscape. This study thoroughly examines collaborative filtering (CF) algorithms and their deployment tactics, crucial for customized recommendations across industries. We review state-of-the-art studies to analyze deployment options’ effects on system performance, including user-combining CF, matrix factorization, nearest-neighbor techniques, and deep learning architectures. Each approach presents unique benefits and trade-offs in accuracy, scalability, and complexity. Despite advancements, challenges like cold start and scalability persist, necessitating ongoing research. Our study underscores the significance of deployment techniques in enhancing CF-based recommendation systems, providing insights to academia and industry for creating more effective systems and delivering tailored digital experiences.