We've been covering the ongoing race to claim the $1 million Netflix Prize
for a while now, highlighting some surprising
methods for attacking the problem. Every time we write about it, it appears that the lead teams have inched just slightly closer to that 10% improvement hurdle, but progress has certainly been slow. Clive Thompson's latest NY Times piece looks at the latest standings
, noting that the issue now is "The Napoleon Dynamite problem."
Apparently, the algorithms cooked up by various teams seems to work great for your typical mainstream movies, but where it runs into trouble is when it hits on quirky films, like Napoleon Dynamite
or Lost in Translation
or I Heart Huckabees
, where people tend to have a rather strong and immediate love
reaction to those films, with very little in-between. No one seems quite sure what leads to such a strong polar reaction, and no algorithm can yet figure out how people will react to such films, which is where all of the various algorithms seem to run into a dead end.
Some folks believe that's just the nature of taste
. It really can't just be programmed like an algorithm, but takes into account a variety of other factors: including what your friends think of something, or even if you happened to go see that movie with certain friends. Basically, there are external factors that could play into taste, that isn't necessarily indicated in the fact that you may have liked some other set of quirky movies, and therefore you must love Napoleon Dynamite
. In some ways, it makes you wonder if we're all putting too much emphasis on an algorithmic approach to the issue, and if other recommendation systems, including what specific friends think of a movie might be more effective. Of course, Netflix is hedging its bets. It's been pushing social networking "friend recommendation" features for a while as well.