Let's say I want to determine the probability that I will upvote a question on SO, based only on which tags are present or absent.
Let's also imagine that I have plenty of data about past questions that I did or did not upvote.
Is there a machine learning algorithm that could take this historical data, train on it, and then be able to predict my upvote probability for future questions? Note that it must be the probability, not just some arbitrary score.
Let's assume that there will be up to 7 tags associated with any given question, these were drawn from a superset of tens of thousands.
I hope that it can make quite sophisticated connections between tags, rather than each tag simply contributing to the result is a "linear" way (much as words do in a Bayesian spam filter).
So for example, it might be that the word "java" increases my upvote probability, except when it is present with "database", however, "database" might increase my upvote probability when present with "ruby".
Oh, and it should be computationally reasonable (training within an hour or two on millions of questions).
What approaches should I research here?