Recommendation Systems (or Collaborative Filtering) tries to match one person's interests based on the interests of a large collection of other users. At first this seems like an easy task, just trying to match vectors. But the simple ideas don't work very well. The AI community have developed much more sophisticated techniques that have been implemented in companies that take recommendation systems seriously, like Amazon and Netflix. Apparently these techniques have reached the point of diminishing returns, thus the contest.
I understand that Amazon wants to sell more stuff, but why does Netflix take the problem so seriously, to the point of having a VP of recommendation systems as well as running this contest? They only recommend to already paying subscribers, the amount of extra business they get or keep by a strong recommendation system seems minimal.
But I shouldn't complain. Too often the public thinks of computer science as simply writing programs and making them run quickly. The Netflix contest sheds light on a different view of CS that shows the depth in a seemingly simple problem.
The million-dollar prize puts recommendation systems in the same class as the P versus NP Millenium Prize. Though if you could show P = NP by giving a quick algorithm for NP-complete problems, you can use that algorithm to develop a great recommendation system and collect a cool two mill.