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From Will C <w...@infomofo.com>
Subject Re: Recommendation scores from LogLikelihood Similarity recommender
Date Mon, 16 Apr 2012 18:35:59 GMT
Thanks for clearing that up.

On Mon, Apr 16, 2012 at 2:02 PM, Sean Owen <srowen@gmail.com> 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 <will@infomofo.com> 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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