Winning with Wikinomics

Another milestone in Wikinomics history occurred recently when the online movie rental company Netflix concluded a three-year contest for a system to improve movie recommendation accuracy on its site by 10%. Two global teams ended in a virtual dead heat, with no winner of the $1 million prize to be declared until September. As the New York Times reported, “The contest, which began in October 2006, has already produced an impressive legacy. It has shaped careers, spawned at least one start-up company and inspired research papers. It has also changed conventional wisdom about the best way to build the automated systems that increasingly help people make online choices.”

Recommendation engines predict what a person might enjoy based on statistical scoring of that person’s stated preferences, past consumption patterns and similar choices made by many others. The goal was to improve the movie recommendations made by its internal software by at least 10 percent, as measured by predicted versus actual one-through-five-star ratings by customers. By a Nose at the WireWhen one team announced last month that it had passed the 10 percent threshold, it set off a 30-day race, under contest rules, for other teams to try to best it. That led to another round of team-merging by leading rivals who assembled a global consortium of about 30 members, appropriately called the Ensemble.