J2EE folks should definitely check out JDO as a better way to develop for the cloud. With JDO, you can stay relational or move to EC2 or GAE without making a big code commitment.
Infinite scalability isn't the only snake oil in the cloud. Other cloud computing myths include "all you need is a credit card" and "cloud is cheaper."
It's no longer language constructs, data structures, or algorithms that are cutting edge. Innovation has moved on to more fertile pastures. Yes, those who build software tools, libraries, IDEs, and compilers will continue to innovate. They have and will continue to come up with some brilliant stuff. But cutting edge developers don't pick a shop because they write in groovy or whatever the language-de-jeur is. Cutting edge developers go where they believe the next killer app is going to be born.
The best developers are multi-lingual. They don't identify with a single programming language. They're not VB developers or Java developers or even Rails developers. They can pick up any language/library/environment quickly. They don't really get off on curly braces versus colons. What feeds the best developers is the challenge of world domination through innovation.
Change the world, right?
Why should corporations care? Two words "litigation exposure." A bot-net living in your network takes down an e-commerce site for day. They will see you in court. Good luck with that "don't blame me, blame my ISP" defense.
I think that kind of "not my problem" thinking is what is driving the current cloud computing craze. Corporations seem to think that they can side step the accountability hassle if they outsource IT to the cloud. Good luck with that too.
When it comes to recommendation systems, everybody's looking to increase accuracy: the Netflix Prize was awarded last July for an algorithm that improved the accuracy of the service's recommendation algorithm by 10 percent. However, computer scientists are finding a new metric to improve upon: recommendation diversity. In a paper that will be released by PNAS, a group of scientists are pushing the limits of recommendation systems, creating new algorithms that will make more tangential recommendations to users, which can help expand their interests, which will increase the longevity and utility of the recommendation system itself.
Accuracy has long been the most prized measurement in recommending content, like movies, links, or music. However, computer scientists note that this type of system can narrow the field of interest for each user the more it is used. Improved accuracy can result in a strong filtering based on a user's interests, until the system can only recommend a small subset of all the content it has to offer.
1 + 1 = 3, for large values of 1.