On 24/02/2010 00:28, Ted Dunning wrote:
so it is going to maximize the distance between the prediction and dissimilar users, and minimize otherwise.For many applications where averages seem usable with positive weights, I would use squared distance from positive examples and negative squared distance from negative examples.
is it something like this? argmin sum(c*(p-r)^2) where c is the correlation, p is the prediction and r is the true rating. T
