In this article, we explore how real-time recommendations support various use cases, including product recommendations and logistics. A graph database is used to store customer purchase history, which can be queried to suggest popular products or personalized recommendations based on individual customer behavior and social connections. The query examples demonstrate how Neo4j's Cypher language can be used to extract insights from the graph data, such as recommending historically popular purchases made by a customer and suggesting products purchased by their friends and friends-of-friends. By leveraging graph technology, organizations can incorporate customer feedback, adjust for seasonal trends, and provide personalized recommendations in real-time without complex coding or relational JOIN issues.