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From Sean Owen <>
Subject Re: Recommendation scores from LogLikelihood Similarity recommender
Date Mon, 16 Apr 2012 18:02:08 GMT
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

On Sun, Apr 15, 2012 at 5:47 PM, Will C <> 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?

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