Graph databases like Neo4j are revolutionizing the way companies offer online products or services by enabling them to build highly sophisticated recommender systems. These systems can analyze customer behavior and preferences in real-time, providing personalized recommendations that maximize revenue. Graph databases give equal prominence to storing both data and relationships between entities, allowing for rich semantic context and fast query performance. This enables companies to make finely-tuned recommendations that cater to individual customers' interests and preferences, rather than relying on aggregate best-sellers. With the advent of graph databases, organizations can transform their online business with powerful recommender systems like Walmart's product recommender system and SNAP Interactive's dating app, which use Neo4j-powered engines to deliver fast response times across large social graphs.