This comprehensive review tracks the transformation of recommender systems from theoretical concepts to practical applications across multiple domains.

• Traditional to Advanced: Evolution from content-based and collaborative filtering to deep learning, graph-based models, and LLMs
• Cross-Domain Applications: Implementation across medical, educational, and security contexts
• Practical Focus: Bridges the gap between academic research and real-world deployment
• Future Direction: Highlights emerging trends and next-generation recommendation technologies

In medical contexts, these advances enable personalized treatment recommendations, improved clinical decision support, and better patient engagement through tailored health information.

A Comprehensive Review of Recommender Systems: Transitioning from Theory to Practice