djzimm wrote:I'm somewhat new to using Coollector, but am very much enjoying it! I've rated over 400 or 500 movies
djzimm wrote:Where do the reliability numbers come from?
Have you clicked the link above? I've explained it the best I could, please tell me if something isn't clear enough.https://www.coollector.com/help.html#recommendations
When the collaborative filtering
algorithm finds a lot of correlations to analyze, it can produce a prediction with a high reliability. But the reliability may be low because you haven't yet rated enough movies for the system to really understand your tastes. Or because the movie isn't popular enough among the other users to get enough hints about how much you'll like it. Or maybe you have very special tastes and it's hard to find other users with the same mindset (as an extreme example, if you rate completely at random, the prediction engine will of course have a hard time to understand you).
Not all predictions are equal, obviously, and I'm proud that my system is open about that. I don't know any other system which discloses that kind of information. I'm truly passionate with recommendation systems, and I hope I didn't go overboard with that feature. It may sound a little bit technical and I hope it won't scare away people (discard it if that's your case), but in my opinion it's a fundamental aspect of the predictions, and very useful too! For example setting a higher reliability treshold (in the settings) can clean up your list of recommendations by keeping only the most essential recommendations. The realiability is also used by the Rating Helper which will ask you to rate first the movie with the highest reliability, because it's the movie that you're the most likely to have seen.
In all modesty (and until I'm proven wrong), my system is the Rolls-Royce of the recommendation engines, which isn't a small feat because there are a lot of big players in the field