The relevant items, the top 16, are a set. You find how many of the recommendations fall in that set. For precision, ordering does not matter.
You are right that the metric kind of falls apart for users with very few data points. You want to use precision at a small number, and perhaps ignore the results on users with little data. On Thu, Aug 9, 2012 at 5:20 PM, ziad kamel <[email protected]> wrote: > Thanks Sean ! > > Please correct me , when selecting the 16% items we use the top items > , but when comparing with the recommended items we don't use sorted > list . In other words we just compare 2 lists? > > How mahout deal with these 2 cases? > > Case 1: user have many items. Assume 1000 item , so if we recommend 5 > good items from the 160 items we will get a precision of 100% ? is > that ok ? > > Case 2: user having less than 7 items. Assume 5 items, in this case > there won't be top items in the list so the user won't get any > recommendation and no precision ? Do we need to select another > threshold like 50% ? > >
