In this article, Chris McKinlay says he used AdaBoost to choose the proper "importances" of questions he answered on okcupid.
If you haven't read and don't want to read the article, or are unfamiliar with okcupid and the question system, here's the data and problem he had: The goal is to "match" as highly as possible with as many users as possible, each of whom may have answered an arbitrary number of questions. These questions may have between 2 and 4 answers each, and for the sake of simplicity, let's pretend that the formula for a match% $\ M $ between you and another user is given by
$\ M = Q_a/Q_c $
Where $\ Q_c $ is the number of questions you and the other user have in common, and
$\ Q_a $ is the number of questions you both answered with the same value.
The real formula is slightly more complex, but the approach would be the same regarding "picking" a correct answer (he actually used boosting to find the ideal "importance" to place on a given question, rather than the right answer). In any case, the point is you want to pick a certain value for each question, such that you maximize your match% with as many users as possible - something you might quantify by the sum of $\ M $ over all users.
Now I've watched the MIT course on AI up to and including the lecture on boosting, but I don't understand how you would apply it to a problem like this. Honestly I don't even know where to begin with choosing rules for the weak learners. I don't have any "rules" about what values to choose for each question (if the user is under 5'5, choose A, etc) - I'm just trying to fit the data I have.
Is this not the way boosting is supposed to be used? Is there likely some other optimization left out of how he figured this out?