85 karma · joined January 25, 2012
If Americans can drive and talk on their cell phones, they can handle a roundabout.
EDIT: Just to point out why this is a big deal - What percentage of new sites are mostly static, presentational? Probably a slim minority. Whilst Github can't code your site for you (yet?), giving you the tools to develop your app layer and frontend - with some as-of-yet seen integration tools? - those are some very large slices of the pie. Don't forget Github pages is currently free too, perhaps there'll be a paid tier for dynamic sites.
For example, 5 stars for quality and 0 for rewatachability doesn't tell me I should watch the film if I haven't already. But maybe 2 stars for rewatability/subjective enjoyment is enough justification to watch it to appreciate the quality?
If you factor your vector weights by the scale of your quality rating (0 - 10?) then if user A liked the quality film X vs. film Y +6 more points than user B's +1, this would give you a more accurate correlation.
Anyway, food for thought. A very fun problem to be working on!
Say we start at 0 and user A likes the next move in both directions (+4) and user B only likes it more in one direction and the same in the other (+2) then you're still going to get a positive correlation, just a slightly lower one than if both users liked it in both directions.
R([0, 4, 4], [0, 4, 4]) = 1.0
R([0, 4, 4], [0, 2, 4]) = 0.852
R([0, 4, 4], [0, -4, -4]) = -0.9
So say.. "Same in both dimensions" = 0 "Same quality but more rewatchable." = +1 "Same quality but less rewatchable." = -1 "Higher quality but less rewatchable." = +2 "Higher quality but same rewatchability." = +3 "Better in both respects." = +4 etc..
Then you could pass those to a coefficient like Pearson's R.
x = [0, 1, 2, -1, -3, 4, -4] y = [0, 1, 1, 2, -1, -2, 0]
It'd be an interesting experiment to see what results that gives vs. your current algorithm.