In the case of no ratings, the value you observe is *not* a predicted rating. After all, they are all 1.0 and so can't be used for ranking. The result is actually a sum of similarities, which is why it can be arbitrarily large. It is not supposed to be in [0,1] or anything like that.
On Sun, Apr 15, 2012 at 5:47 PM, Will C <[email protected]> wrote: > I have a boolean input dataset, with user, item, and preference. Each > preference is a 1.0 if it exists. Based on this dataset I had used a > Tanimoto Similarity and tried both Boolean Pref User and Item Recommenders. > > > After reading Mahout in Action and several threads on stack overflow, I saw > that the LogLikelihood Similarity model was recommended for boolean dataset > recommenders. > > However, the scores I get for the recommended items using the LogLikelihood > similarity are sometimes much greater than 1.0, even though none of the > input scores are higher than that. I saw scores of 11.0 being returned for > some users' recommendations. > > This is making it very hard for me to use the scoring and estimation > functions. I have switched back to Tanimoto for now, but am I doing > something wrong, or am I incorrect in expecting the recommended scores and > estimated preferences to be in the 0-1.0 range for this dataset?
