Overstock.com and RichRelevance Offer $1 Million Prize to Speed Innovation in Retail Personalization
RecLab Prize on Overstock.com challenges researchers to advance the state of the art in product recommendations with new privacy-secure cloud environment
San Francisco - May 12, 2011 - Overstock.com and RichRelevance® 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 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.
... 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.
See also Geekwire A $1M prize for the best product recommendation algorithm.