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

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 present a real-time recommendation engine built on our graph data platform that provides the following extensions to a basic recommendation model: True real-time recommendation algorithms: We provide a simple framework for customers to author and deploy real-time recommendation algorithms with no pre-computation required. Streaming updates of user behavior and product information: As quickly as data are generated, the recommendation engine applies the updates and can serve updated results. Support for offline recommendation algorithms}: Users with existing investment in a quality recommendation program can import their pre-computed results into the graph database for efficient, unified service of results. Tools for Business-centric requirements: The engine offers a range of weighting, sorting, and filtering options to tailor recommendation algorithms to business needs. For example, the engine can eliminate products that are out of stock or favor products that are known to perform well in different real-time contexts. Multiple algorithm ensemble support: There is rarely a case where one algorithm is sufficient to identify the best items to recommend to a user. Integrating points 1 through 4, the engine provides intuitive methods for specifying and combining the results of multiple recommendation algorithms to achieve the highest-performing results. Recommendation feedback tools: Pre- and Post-analysis tools, built around a business' logic, are used to generate reports to assess the value of both potential and currently deployed algorithms. Of particular interest to RecSys attendees, 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.