Assassinator Wrote:What needs to be done is that instead of each anime being scaled according your distribution, it needs to be first scaled with the distribution of all the other scores for that particular anime (to get rid of "good anime gets good scores form everyone"), then scale that result with your own distribution.  This way it should give better correlation figures.... if stuff doesn't get warped too much.

Not terribly sure that matters so much for correlation.  If both people rate it high, it's positively correlated.  Scale it down, and there's still positive correlation.  Pearson already takes this into account.
The scaling is just how they decided to do it.  I can't say anything wrong about it really.
What I'm saying is that there's plenty of other issues not regarding the numerical manipulations used:
- it's based on things you've watched in common; two people who watch very different things most likely have low compatibility since they probably like different stuff, but if they happen to rate some common stuff they saw in a consistent manner, they'll end up with a high score
- as above, distortion from small samples?

But coming up with a good statistic is always difficult anyway.

Assassinator Wrote:Oh, and that also might be quite computationally intensive.  I mean some anime have like 50000 votes on it, and lets say you have 100 anime in your list that matches... sounds heavy.  Although since I'm not much of a programmer, I can't really say for certain (ask Zinga), but it's probably not that viable.

Nah, voting average scores are easy to calculate.  Typically they just store the total and number of votes.  Average score = total ÷ num_votes.