Product recommendation techniques rely on detecting existing patterns in data, such as user behavior, product attributes and their interactions and applying them for other candidate users and products. TigerGraph's graph database can identify user preferences and product associations, enabling the delivery of prompt and accurate recommendations. It navigates the network of interconnected data points, revealing meaningful patterns for tailored product suggestions.Unlike traditional databases that are limited by table joins, TigerGraph efficiently manages extensive query loads. Its ability to explore deep relationships, often up to 10 hops or more, in real-time is a game-changer for product recommendation systems.