Virtual Video Store Clerks Take On The Geeks For Netflix Prize

from the wisdom-of-the-movie-geeks dept

We've been fascinated with the Netflix Challenge for a while now. That's Netflix's offer of $1 million to whomever can improve on their system for recommending movies by 10%. While there were a lot of early success stories in making improvements, all of the attempts seemed to bog down, making much more gradual improvements, but not getting close enough to hit that 10% mark. Earlier this year, we wrote about the surprising run up the leaderboard by a (previously) anonymous individual who approached the problem from a very different perspective, that of a psychologist, rather than a coder, which apparently was quite helpful in getting good results through a very different method.

Now some other folks are trying something completely different, relying on more of a "crowdsourcing" system, combined with a gaming element. They've set up a virtual video store, called Video Store Clerk, and set it up as a game for movie buffs. The game players act as a video store clerk, and can see how particular users rated three movies, and are then asked to predict how they would rate a fourth movie, with points given to correct answers. The idea is that they'll be able to use these crowdsourced predictions to create an even better model than the purely algorithmic model being worked on by various teams.

This reminds me of the research work by Luis von Ahn to do things like tag images via the "ESP Game." von Ahn has had numerous successes in creating fun casual online games, where the output data is actually very useful for taking on some sort of problem that is quite difficult to do algorithmically (such as identifying what's really in an image). The real question, though, is if movie recommendations really work that way as well. Perhaps I need to be a bigger movie buff, but so far, I'm not particularly good at figuring out how others would rank a movie. And, unlike the ESP game, frankly, the Video Store Clerk game isn't all that fun as currently designed. After playing it once, I had no desire to try again. Still, I'm intrigued by the different approach, and wonder if a more advanced (and more fun) version might be much more effective.

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