(Changed subject from unrelated thread) You measure precision / recall, or the related F1 measure, or normalized discounted cumulative gain, or ROC. They are different, standard metrics that are less complicated than the sound.
On Fri, Jul 6, 2012 at 6:13 PM, Razon, Oren <oren.ra...@intel.com> wrote: > Thanks, it helped! > > After having some thoughts about what the outcome prediction, I'm having a > question about measuring the quality of my model. > If I'm using a technique in which in the end I'm predicting a preference > value (implicit \ explicit) I could easily measure my model by applying it on > a test dataset and calculating RMSE and etc. > But if I'm just estimating the possibility the user will like the item (such > with the co-occurrence item based), it give me the ability to rank items, but > how could I estimate my success? > How could I measure the success of my ranking? > >
