Translating User Recommendation Techniques Across Domains: Algorithms, context awareness and Cross-Sector Opportunities

This paper presents modern personalized recommendation systems that leverage graph database technology and large language models to provide context-aware, real-time suggestions. It discusses hybrid algorithms combining clustering and vector-based techniques to enhance user experience. Key contributions include addressing challenges such as data sparsity, cold start, scalability, and bias mitigation, through ensemble models and explainability frameworks, demonstrating applicability across multiple industries beyond media streaming.