Hi Ziad, I did answer your last question on this list -- don't see this one previously though.
The "relevant" items are chosen as those whose pref value exceed some given threshold. The default threshold is the mean of all 100 pref values plus one standard deviation. Assuming the prefs are about normally distributed about the mean (a significant assumption), and because 84% of the data should therefore fall below mean plus 1 standard deviation, that means you pick about the top 16% (16 of 100) items as relevant. Yes your interpretation of precision is correct. On Thu, Aug 9, 2012 at 4:12 PM, ziad kamel <[email protected]> wrote: > Hi , I asked this question few months ago with no answer. Hopefully > someone can help . > > When not using a threshold, the default is to use average ratings plus > one standard deviation which equals to 16%. Assume that a user have > 100 items. Does that mean that his good recommendations are the top 16 > items ? In case we use precision at 5 , we going to select only top 5 > items from the 100. So is the precison going to be how many among the > 16 items are in the 5 items ? Assume that we get 4 from the 16 in list > of 5 , the precision will be 80% ? > > IRStatistics stats = evaluator.evaluate(recommenderBuilder, null, > model, null, 5, > GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD, 1.0); > > Thanks ! >
