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Example suggestions from the MyRue recommendation engine

Rue Gilt Groupe offers online shoppers a unique retail experience through its flash sale model. Rue Gilt Groupe isn’t your typical online shopping site. And that’s the point. The company provides its members with a dynamic online shopping experience that’s different each time they visit the site.

To continually differentiate the company from competitors and bring more value to members, the Rue Gilt Groupe team turned to personalization. “Our goal was addressing our core customers. We wanted them to know that we understand them as a customer and we can provide them with suggestions that we believe they’re really going to like,” says Stephen Harrison, data science architect at Rue Gilt Groupe.

After careful evaluation of its distinctive data requirements and model specifications, Rue Gilt Groupe leveraged the alternating least squares (ALS)-powered Collaborative Filtering (CF) implementation within the Apache Spark MLlib package to build and run its recommendation engine algorithm. The team needed to identify a service with complex in-memory processing and highly flexible scalability. It had to be capable of running massive data sets while still being easy for Rue’s developers to use.

The company decided to build MyRue, a Collaborative Filtering (CF) recommendation engine, on AWS. For help, they turned to Databricks, an AWS Partner Network (APN) Advanced Technology Partner and AWS ML Competency Partner. The team chose to run its recommendation engine on the Databricks Unified Analytics Platform for many reasons, including its cost effectiveness, flexibility, speed, and interoperability with AWS services.

Rue Gilt Groupe is now able to provide users with a more personalized browsing experience. For the Rue Gilt Groupe team, the ease with which they’ve been able to run the MyRue recommendation engine has been crucial. “Our architecture just works,” says Harrison. “We haven’t had a single failure in the eight months the service has been up.” The team has been particularly impressed by the sheer amount of data that AWS Batch can process and the scalability of Amazon DynamoDB.

“Deciding that you want to double your capacity and simply being able to do it—that never gets old,” says Harrison. “And we’re using it as a competitive advantage. Leveraging the managed services on AWS have allowed us to be agile. They’re the strongest tool in our tool belt. The ease with which we’re able to be agile is huge for us in our efforts to deliver a consistent and unique user experience.”

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