Graph databases are being used by companies to solve complex problems, such as real-time recommendations. These databases can combine a customer's browsing behavior and demographics with their buying history to provide relevant recommendations. Graph technology is particularly useful for connecting masses of buyer and product data, allowing for the analysis of relationships between entities and understanding the quality and strength of those connections. In particular, eBay uses graph technology, powered by Neo4j, to generate real-time recommendations that take into account contextual information and natural language understanding. This approach enables the creation of a real-time recommendation engine that can understand and learn from shopper interactions, providing personalized product suggestions. The use of graph databases in this way is becoming increasingly important as consumer expectations for relevant and timely suggestions continue to grow.