I'm thinking of writing an app to classify movies in an HTPC based on what the family members like.
I don't know statistics or AI, but the stuff here looks very juicy. I wouldn't know where to start do.
Here's what I want to accomplish:
Compose a set of samples from each users likes, rating each sample attribute separately. For example, maybe a user likes western movies a lot, so the western genre would carry a bit more weight for that user (and so on for other attributes, like actors, director, etc).
A user can get suggestions based on the likes of the other users. For example, if both user A and B like Spielberg (connection between the users), and user B loves Batman Begins, but user A loathes Katie Holmes, weigh the movie for user A accordingly (again, each attribute separately, for example, maybe user A doesn't like action movies so much, so bring the rating down a bit, and since Katie Holmes isn't the main star, don't take that into account as much as the other attributes).
Basically, comparing sets from user A similar to sets from user B, and come up with a rating for user A.
I have a crude idea about how to implement this, but I'm certain some bright minds have already thought of a far better solution already, so... any suggestions?
Actually, after a quick research, it seems a Bayesian filter would work. If so, would this be the better approach? Would it be as simple as just "normalizing" movie data, training a classifier for each user, and then just classify each movie?
If your suggestion includes some brain melting concepts (I'm not experienced in these subjects, specially in AI), I'd appreciate it if you also included a list of some basics for me to research before diving into the meaty stuff.