Solving Data Challenges with Knowledge Graphs and Context-Aware Recommendation Systems

Recommendation systems have advanced in recent years, but organizations still grapple with heterogeneous, sparse or insufficient data. These issues can cause bottlenecks for generating highly-personalized recommendations. In this talk we will discuss how to solve some of these challenges by building context-aware recommendation systems with knowledge graphs and graph data science.
Katie is a Data Science Solution Architect at Neo4j. She completed her degree in Cognitive Neuroscience at Harvard University. Passionate about people and problem solving, she transitioned to focusing on helping people and businesses leverage data for impactful outcomes as a customer-facing data scientist.
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