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So important is the optimum engine that, in 2009, Netflix gave a $1 million competition prize to an outside group that successfully helped it improve its in-house CineMatch recommendations software by just 10%.
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But several crucial techniques garnered from the contest have been folded into the company's in-house movie recommendation software, Cinematch, and customer retention rates have improved slightly.
Hunch will keep its operations in New York and continue to experiment with developing recommendation software while it begins integrating some of its tools into eBay's site.
And auctions could get even smarter: Net Perceptions, a software company, has already developed "recommendation software" that looks at what a buyer has bid on (successfully or unsuccessfully) in the past.
It was announced this week that Netflix had awarded its one-million-dollar prize for a new movie-recommendation software to a multinational team of statisticians and computer engineers calling themselves BellKor's Pragmatic Chaos.
By Jon Michaud September 22, 2009 It was announced this week that Netflix had awarded its one-million-dollar prize for a new movie-recommendation software to a multinational team of statisticians and computer engineers calling themselves BellKor's Pragmatic Chaos.
For example, contestants in Netflix's competition to improve its recommendation software received a training data set containing the movie preferences of more than 480,000 customers who had, as they say in the trade, been "de-identified".
The three-year competition for the first Netflix Prize, which began in 2006, offered $1 million to the first group that could improve the predictive accuracy of the company's movie recommendation software by at least 10 percent.
Last fall, Netflix awarded $1 million to a team of statisticians and computer scientists who won a three-year contest to analyze the movie rental history of 500,000 subscribers and improve the predictive accuracy of Netflix's recommendation software by at least 10 percent.
Last .fmfocuses on their music recommendation software, with user generated content secondary.
Then it created a sales recommendation software engine that proved that online retailers could sell as much as fulfill.
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