SAN FRANCISCO, May 12, 2011 /PRNewswire/ -- Overstock.com (short cut: O.co) and RichRelevance® today unveiled the RecLab Prize on Overstock.com. The Prize provides a cash award totaling up to $1 million to the researcher or research team who can achieve a measurable lift over existing product recommendations in a wide variety of shopping contexts on Overstock.com. The RecLab Prize rewards the highest performing individual or team based on the results they are able to deliver within a defined judging period (up to $1 million for a 10% or greater lift). Complete details about eligibility for registering and competing for the Prize are available at http://overstockreclabprize.com/.
RecLab Prize contestants gain immediate access to a high-quality and comprehensive synthetic dataset via RichRelevance's open-source RecLab project, a highly scalable platform for research code. The RecLab approach enables researchers to develop their algorithms against synthetic data and then test against real data. Top performing algorithms will be exposed to real data and will run live within the RichRelevance cloud environment (as real product recommendations to Overstock.com's customers). This groundbreaking approach enables researchers to solve a real-world problem with real-world constraints, while never exposing data to an outside system, thereby preserving data security and eliminating privacy concerns.
"We are excited to support the academic research community, bringing state-of-the-art analytics to our business through our partners at RichRelevance," said Overstock.com CEO Patrick Byrne. "This is a phenomenal opportunity to benefit our customers, who will get early, exclusive access to the most advanced recommendations possible through the participation of top educational and research institutions worldwide."
Online product recommendations are among the shopping tools most widely used by consumers who need to easily find relevant and enticing products from the millions available online. Overstock.com has worked with RichRelevance since 2009 to present shoppers with dynamic recommendations that grow smarter over time and accurately reflect more than 60 different ways that people shop on the site (by price, by brand, by category). Now Overstock.com is partnering with RichRelevance to open up this real-world business challenge to researchers. The RecLab Prize on Overstock.com breaks rank with previous prizes to present researchers with a complex, multi-dimensional problem facing retailers – not to simply predict how a consumer will rate a product but to effectively pinpoint the most appropriate product array to show shoppers at any point in the research and purchase process. The Prize demands that researchers craft approaches that quickly identify and adapt to contextual clues and maximize every available piece of data.
"The Netflix Prize did a great job of mobilizing the research community around a new and interesting problem. The RecLab Prize on Overstock.com takes the next step by offering researchers the chance to solve a multi-dimensional, real-world problem and see how their best algorithms perform when put in front of live shoppers," said Darren Vengroff, RichRelevance Chief Scientist and RecLab creator. "The academic community has been clamoring for access to live real-world data and live user interaction to drive their research, and we are giving them more and better access than ever before using a unique approach that absolutely preserves the privacy of shoppers."
A board of judges, including senior engineers at RichRelevance, Overstock.com, and well-known members of the machine learning community will determine the prize winners. In order to win the $1 million prize, a researcher or team must deliver at least a 10% lift over existing product recommendations on Overstock.com. If no one in the round hits this mark, then the judges will award a pro-rated prize to the team who achieves the highest lift as a percentage of the lift they achieve. For example, if the winning team achieves an 8% lift, it will receive $800,000.
In addition, should the winning team be affiliated with an educational institution, RichRelevance and Overstock.com will grant a separately funded Institution Prize valued at 25% of the winning prize to the educational institution. The RecLab Prize is also open globally to non-commercial teams.
Overstock.com, Inc. (short cut: O.CO) is a Savings Engine offering brand-name merchandise at discount prices. The company offers its customers an opportunity to shop for bargains conveniently, while offering its suppliers an alternative inventory distribution channel. Overstock.com, headquartered in Salt Lake City, is a publicly traded company listed on the NASDAQ Global Market System and can be found online at http://www.overstock.com and http://www.o.co. Overstock.com regularly posts information about the company and other related matters on its website under the heading "Investor Relations." Overstock.com® is a registered trademark and O.co™ and Savings Engine™ are trademarks of Overstock.com, Inc. All other trademarks are the property of their respective owners.
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 headquartered in San Francisco, with offices in Seattle and London. For more information, please visit www.richrelevance.com.
This press release contains certain forward-looking statements within the meaning of Section 27A of the Securities Act of 1933 and Section 21E of the Securities Exchange Act of 1934. Such forward-looking statements include, but are not limited to, statements regarding the amount of the prizes and the forecasted benefits of the contest results. Our Form 10-K for the year ended December 31, 2010, our subsequent quarterly reports on Form 10-Q, or any amendments thereto, and our other subsequent filings with the Securities and Exchange Commission identify important factors that could cause our actual results to differ materially from those contained in our projections, estimates or forward-looking statements.