Recommendation systems are at the heart of many products we use today, helping us discover new music, expand our wardrobes, and navigate the massive amounts of information on the Internet to answer our search queries. In a world where efficiency and accuracy is paramount, and processing power and availability of user data varies across industries, graph-powered engines and their ability to deliver continued performance at scale provide many advantages.
How are these recommendations determined? What conditions must exist in order to build a graph-based system that produces accurate, relevant results? What technologies are at play under the hood? Through the business use case of a leading national learning management system (LMS) in the healthcare industry, this presentation will answer these questions by providing an overview of the problem statement and the solution architecture, including technical dives into NLP taxonomy enrichment, knowledge graph development, and recommender logic.