It is true that a process based on user-user similarity only won't be able to recommend item 4 in this example. This is a drawback of the algorithm and not something that can be worked around. You could try not to choose this item in the test set, but then that does not quite reflect reality in the test.
If you just mean that compressing the range of pref values improves RMSE in absolute terms, yes it does of course. But not in relative terms. There is nothing inherently better or worse about a small range in this example. RMSE is a fine eval metric, but you can also considered mean average precision. Sean On Wed, May 8, 2013 at 4:29 AM, Zhongduo Lin <zhong...@gmail.com> wrote: > Hi All, > > I am using the Mahout to build a user-based recommender system (RS). The > evaluation method I am using is > AverageAbsoluteDifferenceRecommenderEvaluator, which according to the > "Mahout in Action" randomly sets aside some existing preference and > calculate the difference between the predicted value and the real one. The > first question I have is that in a user-based RS, if we choose a small > number of neighbours, then it is quite possible that the prediction is not > available at all. Here is an example: > > User 1 rated item 1, 2, 3, 4 > neighbour1 of user 1 rated item 1, 2 > neighbour2 of user 1 rated item 1, 3 > > In the case above, the number of neighbours is two, so if we take out the > rating of user 1 to item 4, there is no way to predict it. What will mahout > deal with such a problem? > > Also, I am trying to map inferred preferences to a scale of 1-5. But the > problem is that if I simply map all the preference to 1-2, then I will get a > really nice evaluation result (almost 0), but you can easily see that this > is not a right way to do it. So I guess the question is whether there is > another way to evaluate the preference mapping algorithm. > > Any help will be highly appreciated. > > Best Regards, > Jimmy
