> Now in the future when a blue or heavy thing comes along, I'd like the machine to try to predict whether he'll like it or not. There seems to be two separate

> Now in the future when a blue or heavy thing comes along, I'd like the machine to try to predict whether he'll like it or not. There seems to be two separate steps. First you need to classify the thing a person rates. I'm guessing for each item, its not going to be a simple "blue" classification because this is user generated data so it might have for example 40% "blue" and 60% "heavy". Bayesian theory will do those classifications. p (tag given thing) = p (thing given tag) * p(guess tag) Then you just set a threshold and anything above it is the class for that item. Second, to do recommendations, you're taking into account the user preferences or whether they say they like something or not. You can do this a number of ways, but perhaps the one that is the easiest is just to calculate the similarities between items. For example you would find maybe "blue" and "red" things are highly similar, so you'd recommend "red" items to the user that loved the "blue" items. You can use Pearson Correlation to do that. Check out Collective Intelligence. It's got lots of (python) code and gives a great practical, introductory explanation to these algorithms.