I'm just now looking into RBM's in an effort to apply this to the Netflix prize. I'm getting reasonable results with other methods now. My place is 435 at the moment and pretty soon, I should be under 400. RBM's are a bit more difficult to implement than I imagined, loads of factors and intricate mathematical details. Other than that, I'm not throwing away my other methods. I'd like to see how RBM's can be applied to train on residuals, otherwise known as "those hard to rate movies".
There is at least one error that won't go away and that is the fact that a user can simply decide to rate a movie off by one rating point. If that happens, the error probably ranges from 0.5 to 1.4, thus creating a large difference. There's a very large group that's pretty predictable, but most groups are quite unpredictable in their behaviour (or most ratings are).
Well, on another note. I'm now being tortured with prolog. I really like the language, but hate it at the same time, since I'm so much used to procedural programming. I keep looking for for-loops, list iterations, inserts, deletes and the likes, but prolog doesn't truly have them. There is a bit of procedurality in Prolog however, but it's mostly declarative. The bad thing is that it has a couple of tricks that you need to get used to. Especially the starting phase is tricky, but having used the tricks here and there, the thinking inside the language is developing a bit.
Oh, and I made some new pictures with the camera:
New tool in town: KnowledgeGenes.com
7 years ago