Traditionally, researchers have had to work with isolated data sets in order to protect sensitive consumer data. In an industry-first approach, RecLab enables researchers to test and debug algorithms against synthetic data sets, then run their best algorithms against live data on the world’s top retail web sites. As an existing customer, Overstock.com is the first retailer to participate in RecLab. Through RecLab, RichRelevance is closing the gap between the research community and the e-commerce industry by facilitating and speeding innovation that brings value to its clients and the industry as a whole.
The initiative was profiled in a recent Fast Company article: “There are many holy grails in online commerce, but one that has frustrated C-level executives and engineers alike is how to produce better recommendation algorithms. Produce better recommendations, and you’ll sell more stuff… [RichRelevance] has come up with a way to speed up the process of finding better math to produce suggestions of things you actually might want to buy.” Read the full article here.
“Every 50 milliseconds a shopper interacts with a RichRelevance personalized recommendation across a network of more than 45 of the world's largest retailing sites, including Walmart.com, Sears.com, and Overstock.com,” said RichRelevance CEO David Selinger.
“Given the pace of e-commerce, new ideas and innovations constantly spring forth from different disciplines, which is why the RecLab research community is so vital. Through this innovation, we’re bringing value to our customers years ahead of when it might surface in research or be filtered through a journal," Selinger says.
“There are tons of incredibly smart researchers in universities around the world who are clamoring for ways to test their hypotheses and algorithms against actual consumers,” says Darren Vengroff, chief scientist at RichRelevance and head of RecLab. “These are the same people who spent years working on the Netflix prize. Now we’re giving them the opportunity to go after actual industry challenges, including one of the most basic problems in retail: will someone buy this or not?
He continues, "We’re letting them take their best shot at coding a solution, testing it, ensuring it works, and, through our secure cloud, allowing it to run in a real retail environment. This is a huge spur to innovation, and we’re already seeing tremendous interest in the machine learning community.”