My piece in Wired: The Netflix Prize

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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2 thoughts on “My piece in Wired: The Netflix Prize

  1. Dirty Davey says:

    I wonder if movie recommendation systems are a little better or more interesting than those for music or books because director and/or actor aren’t quite equivalent to musican/group or author.

    On Amazon, I find that–whatever the real underlying algorithm might be–in practice it works like “you’ve bought a couple of Richard Thompson CDs, so now we’ll fill your recommendation list with everything we have by Richard Thompson that you don’t already own”. So in order to see any recommendtions from other artists, you have to go through and mark things as already-owned, which makes Richard even more overrepresented on the computer’s list of things you have/like.

  2. Richard says:

    Neat!

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