<D <M <Y
Y> M> D>

[Comments] (3) : Netflix has a contest going to improve their recommendation engine's ranking by a certain amount. I've gotta say it's the best-specified contest I've ever seen. I'm leery of these contests because the rules usually have a "we own all your ideas even if you don't win" clause. This one just has a "we can use your idea even if you don't win" clause, which is less onerous. And the prize is a cool million, rather than the more traditional prize of a candy bar.

Since I have previously dabbled in recommendation engine design, some friends have asked me if I'm going to go for this prize. Well, I don't really need the money -- just now I got email telling me I'd won $2,500,000.00 -- but more importantly I don't think my Ultra Gleeper ideas are applicable to this domain. They mainly focus on improving the recommendations, whereas this contest is very tightly focused around predicting users' opinions given specific recommendations.

Not to say I don't have ideas. If I were going for the prize I would use IMDB and Amazon data for the movies to gather hypotheses about why people rate a movie high or low. I would then build profiles for the users, using the set of hypotheses as a basis. Then I would predict the users' future actions based on the profile. That sounds like handwaving but the basic idea (which others have seized on as well) is using objective data about the movies, instead of trying to figure out connections between them solely by how Netflix users rate them. And weighted vectors would probably be involved.

Of course, for all I know Netflix already uses external data to run their recommendation engine, so don't just run with this idea and expect to get anywhere.


Unless otherwise noted, all content licensed by Leonard Richardson
under a Creative Commons License.