Mobile captured 18.5% of Black Friday and Cyber Monday digital spending, comScore says.
RichRelevance Launches RecLab To Speed Innovation in Retail Personalization
New open-source project offers researchers the ability to test and validate personalization algorithms in a live shopping environment for the first time in e-commerce history.
San Francisco, CA – Jan. 31, 2011 –– RichRelevance®, the leading provider of dynamic e-commerce personalization for the world’s largest retailers, 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 for the first time ever. 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 websites. 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 <http://www.fastcompany.com/1721659/how-recommendations-could-get-smarter> : “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 <http://www.fastcompany.com/1721659/how-recommendations-could-get-smarter> . “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.”RecLab SpecificsRecLab is an open source project licensed under the Apache 2 <http://code.richrelevance.com/reclab-core/license.html> license. It defines all of the key Java interfaces and APIs for interacting with the RecLab environment. It also provides simple implementations of these APIs that allow developers to design, test, and debug their algorithms quickly and efficiently without having to take the time, effort, or expense of setting up a large cluster of their own.The project supports a wide variety of contextual and behavioral data, both at model build time and at runtime. Code running in RecLab has access to both immediate click-by-click and a wide variety of past shopping behavior. In RecLab, researchers begin with synthetic data sets, derived from probabilistic models, not real shoppers. However, once code has been written, tested, and debugged it can be submitted <http://code.richrelevance.com> to run live against a small segment of traffic on a live retail site through the RichRelevance cloud. More information on the environment, as well as a tutorial on building models within the environment, can be found here <http://code.richrelevance.com> .
RichRelevance powers personalized shopping experiences for the world’s largest and most innovative retail brands, including Wal-Mart, Sears, Overstock.com and others. Founded and led by the e-commerce expert who helped pioneer personalization at Amazon.com, RichRelevance helps retailers increase sales and effectively monetize site traffic by providing the most relevant products, content and offers to shoppers as they switch between web, store and mobile. RichRelevance has delivered more than $1 billion in attributable sales for its clients to date, and is accelerating these results with the introduction of a new form of personalized advertising called shopping media which allows brands to engage shoppers where it matters most – at the point of purchase on the largest retail sites in world. RichRelevance is located in San Francisco, with offices in Seattle and London. For more information, please visit www.richrelevance.com.