Industry Session 3

Date: Monday, Sept 19, 2016, 08:30-10:10
Location: Kresge Auditorium
Chair: Paul Lamere

Adam Anthony

Leveraging a Graph-Powered, Real-Time Recommendation Engine to Create Rapid Business Value

by Adam Anthony (GraphSQL)

Deployment of open source recommendation systems has been shown to be an effective way to increase sale conversions on a variety of e-commerce sites. However, there remains a large gap between deploying the core algorithm provided by these systems and delivering an application-quality recommendation system, specifically tailored to address complex and dynamically changing business needs. We will discuss the technical aspects of a graph-based implementation of the recommendation engine and how it facilitates the rapid design and deployment of an efficient real-time recommendation system under a single service. We will briefly discuss the architecture of our graph database system to show how it can efficiently serve a large user base even from a single server shared-memory architecture. Attendees will also learn about graph-based data modeling and how viewing data from this perspective can lead to new types of business insights and applications that are not easily implemented using traditional relational and/or NoSQL platforms. We will conclude with a brief demonstration of a real deployed application UI to demonstrate the ease by which recommendation systems can be implemented and deployed using the engine.

About the Speaker

Dr. Adam Anthony received his Ph.D. in computer science from the University of Maryland Baltimore County in 2009. His expertise is in data clustering, pattern recognition and computing similarity in graphs. He has been working at GraphSQL for three years, one of 7 early members. His primary role is in the direction and development of a flexible, graph-based recommendation engine with an emphasis on real-time recommendation with business-centric optimization. The recommendation engine is currently used in production systems, delivering revenue-boosting recommendation systems with minimal developer effort.

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