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From Pat Ferrel <pat.fer...@gmail.com>
Subject Re: How to interpret recommendation strength
Date Thu, 15 Nov 2012 19:16:48 GMT
I think I had some double talk there. Again I'm looking for a way to reasonable rank recommendations
between users so globally rank them.

(((sum-of-similarities-for-user)*cooccurrences-for-user)/max-coocurrences-in-model)/max-sum-of-similarities-in-model
?

On Nov 15, 2012, at 10:52 AM, Pat Ferrel <pat.ferrel@gmail.com> wrote:

Thanks, you answered in the code too and I see how it is being used to rank returned recommendations,
which all makes sense.

Maybe I'm looking for something different. For purposes other than ranking it seems like it
would be nice to have value that represents the probability that the recommendation is a true
preference. This would be a value between 0-1, obviously, and if it's available could be used
to compare recommendation strengths between different users.

Another way to approach this magic number, not a probability here, and intuitively speaking
might be to average the similarity of users in the neighborhood who preferred the recommendation
but weight by the number of users and normalize by the max number of user who ever contributed
to a recommendation in the entire model

On Nov 15, 2012, at 10:07 AM, Sean Owen <srowen@gmail.com> wrote:

It's an average similarity, weighted by count -- which is to say, it's a
sum of similarities. This isn't terribly principled but works reasonably in
practice. A simple average tends to over-weight unpopular items, but there
are likely better ways to account for that.


On Thu, Nov 15, 2012 at 5:59 PM, Pat Ferrel <pat.ferrel@gmail.com> wrote:

> Using a boolean data model and log likelihood similarity I get
> recommendations with strengths.
> 
> If I were using preference rating magnitudes the recommendation strength
> is interpreted as the likely magnitude that a user would rate the
> recommendation. Using the boolean model I get values approaching 2 (this
> over a quick and small sample so not sure of the real range), which leads
> me to the question...
> 
> What is the meaning of the strength returned with the recommendation for
> boolean data?



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