I see, but we are removing the good recommendations and we are assuming that the less preferred items by a user can predict his best preferred. For example, a user that has 100 books , and preferred 16 of them only while the rest are books he have read. By removing the 16 we are left with 84 books that it seems won't be able to predict the right set of 16 ?
What are the recommended approaches to evaluate the results ? I assume IR approach is one of them. Highly appreciating your help Sean . On Thu, Aug 9, 2012 at 11:45 AM, Sean Owen <[email protected]> wrote: > Yes, or else those items would not be eligible for recommendation. And it > would be like giving students the answers to a test before the test. > > On Thu, Aug 9, 2012 at 5:41 PM, ziad kamel <[email protected]> wrote: > >> A related question please. >> >> Do Mahout remove the 16% good items before recommending and use the >> 84% to predict the 16% ? >> >>
