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.
Just minutes before the contest deadline, the Ensemble’s latest entry edged ahead on the public Web leader board - by one-hundredth of a percentage point. Even though one team will lose by incredibly little, lessons have been learned by all and careers have been advanced. One group has already founded its own start-up, Gravity, which is developing recommendation systems for commercial clients, including e-commerce Web sites and a European cellphone company. A Contest of Collaboration
The biggest lesson learned, according to members of the two top teams, was the power of collaboration. It was not a single insight, algorithm or concept that allowed both teams to surpass the goal Netflix set nearly three years ago. Instead, they say, the formula for success was to bring together people with complementary skills and combine different methods of problem-solving, including computer scientists from a Hungary, Israel, Austria, as well as math majors at Princeton University, AT&T engineers, statisticians, and machine learning experts and computer engineers. Making Wikinomics Work for You
This story is one of many innovative leaders who are thinking about value creation in new ways, reaching out to “best of breed” thinkers far beyond their own category.
In contrast, focusing solely on what competitors are doing in your industry merely reinforces the imprint of the training and experiences that companies share within a category. At the turn of the last century, buggy whip manufacturers were seeking undoubtedly to learn from each other how to make better whips more efficiently, all the while missing the larger emerging transportation picture.
Engaging experts from a variety of fields and perspectives is an important element in Ideation Nation™ , a creative problem-solving process from Insighting Ideas that brings the active participation of leaders from a variety of fields to focus on your situation. Contributors from around the country or the world sign NDAs before being prepped with background info and participating in the session virtually or asynchronously.
In a faster and simpler way, cross-fertilization can be encouraged through the Idea Ignition™ process that guides your company’s team through thinking about your challenge as though they were outsiders, drawing on their experience in other industries, countries, environments, and times of life. Are you looking for fresh ideas? We at Insighting Ideas are passionate about inciting fresh ways of thinking to help companies break out of wasting time and money by repeating low-return business practices. Contact Insighting Ideas for case studies of problems we have helped companies overcome through collaborative ideation.