The way I see it is that these people have a set of data that one could say is a line on an xy axis. This line goes up, down, etc, and there does not seem to be

The way I see it is that these people have a set of data that one could say is a line on an xy axis. This line goes up, down, etc, and there does not seem to be any pattern. So they come up with a bunch of algorithms that go as near to the line as possible -> they approximate the line with an algorithm. So from that, they can predict how the next step of the function is going to look like. Metadata is like placing some dots on this line and saying "this spot is horror", "this spot is comedy". It becomes irrelevant, because you are already near enough to the line, and that dot does not help you any. If I were dealing with this problem, what I would do is break free of these constraints and concentrate on taking the data as an abstract blob of random, then splitting the individual data (i.e, move data into separate 'dimensions') till I had hundreds of straight lines, and then using those for prediction. But I'm sure they must have tested this already :) I'm rooting for the team with the two jewish guys and the black guy, afterwards they could get together and make a sitcom. Or a joke.