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"Netflix, the popular online movie rental service, is planning to award $1 million to the first person who can improve the accuracy of movie recommendations based on personal preferences. To win the prize, [...] a contestant will have to devise a system that is more accurate than the company's current recommendation system by at least 10 percent. And to improve the quality of research, Netflix is making available to the public 100 million of its customers' movie ratings, a database the company says is the largest of its kind ever released." — More: Netflix Prize.

Dan Frankowski: "[...] [I]t may be possible to link [...] separate identities, because the movies, journal articles, or authors you mention are from a sparse relation space whose properties (e.g., many items related to by only a few users) allow re-identification. This talk examines this general problem in a specific setting: re-identification of users from a public web movie forum in a private movie ratings dataset."

First juxtapositioned on the [create O'Reilly Radar].


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