RichRelevance, a provider of e-commerce personalization for retail, today introduced RecLab, a new open-source project designed to spur innovation in retail personalization. RecLab enables academics, researchers and developers to dynamically test and validate their recommendation algorithms in a live e-commerce environment. Traditionally, researchers have had to work with isolated data sets in order to protect sensitive consumer data.

In what the company claims is 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 websites.

As an existing customer, Overstock.com is the first retailer to participate in RecLab.

"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."

"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," said 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? 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."

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