paulofilmo wrote:[...] Seriously, these results are stunning. They really look (and quite personally) like my kind of films. I'm going to create a 'Quicky' category in my IMDb 'My Movies'. [...]
I'm glad you seem to like it! Be sure to keep me informed on your experiences in the long run.
paulofilmo wrote:[...] Do you know why the first two films under 'Possibly not available for rent' have such high percentages? I'm consumed in the prospect that I could rate a film perfect. [...]
You shouldn't think of a movie having '100%' as perfect. My program normalizes the rankings such that the top ranked movie has '100%', the lowest ranked movie has '0%', and the movie that is ranked exactly in the middle has '50%'. A movie having '100%' only means it has the best chance at pleasing you. It's all about probabilities, really .
paulofilmo wrote:[...] Would you be able to tell me what correlations/Final fit weights on the first page mean? I think I'll be able to decipher the dots, dashes and arrows by comparing the films. [...]
I guess I should make a fourth page with some explanations, eh?
Ok, let me explain correlations with two examples.
1) Let's say you order 10 cars by their size, along with their price tag. I guess you'll agree with me that generally the bigger the car, the more expensive it will be. But I'm sure you'll also agree that that is not always the case, since the price depends on more than just size, for example: luxury accessories, brand, etc. What this means is that the correlation between a car's size and its price tag will be somewhere between 0 and 1. If it were 1, the correlation would be perfect and a bigger car would always be more expensive than a smaller one. If it were 0, you would have no way of telling whether a car would be more expensive based on its size.
2) Let's say you order 10 microchips by their size, along with their price tag. Unlike the first example, here the chips will usually be more expensive when they're smaller. So the correlation between a chip's size and its price is negative, somewhere between -1 and 0.
Here you can find a picture that shows graphs of variables with different correlations. I would guess that my two examples will be best illustrated by the lower two graphs. The left one being the cars, the right one being the chips.
So in my program I test how well the correlation is between your ratings and certain characteristics of a movie that can be quantified. If your PSI correlation would be 1, this would mean that if movie A has a higher PSI than movie B, you will always rate movie A higher than movie B. And if your Age correlation would be -1, this would mean that if movie A is more recent than movie B, you will always rate movie B higher than movie A.
These correlations only show how your ratings are influenced by each parameter on its own. It's very hard to determine how your ratings will be influenced when you combine all of these parameters, because there are also correlations between the parameters. For example, there will usually be a pretty strong correlation between your PSI's and your favourite genres, because your PSI's will already favour movies with those genres that you like.
My program tries to find the combination of weights for the parameters that gives the best fit to your ratings. So the percentages that you see in the right column show how heave each parameter counted towards a movie's ranking in the system.
I hope that made it somewhat clear?