Last month I wrote an article for Wired about the Netflix Prize; a competition to develop a better algorithm for recommending movies, with $1 million from Netflix as the incentive. This kind of problem is immensely hard: the set of ratings submitted by Netflix users is huge, but very sparse (most users haven’t rented most movies) and very noisy (people make mistakes, their tastes change with time, multiple people may be rating on one account.) So to be able to massage this data into a decent set of movie recommendations is a formidable task — as you probably already know from the typically unsatisfactory performance of the recommendation engines that Netflix, Amazon, and so on, use now.
Anyway, the article’s now online; I write a bit about the mathematical techniques that the experts in the area use to attack this genre of problem, and one very interesting non-mathematician with a different and nearly as successful approach.
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