Thanks for clearing that up. On Mon, Apr 16, 2012 at 2:02 PM, Sean Owen <[email protected]> wrote:
> 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? >
