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From Sean Owen <sro...@gmail.com>
Subject Re: Average Absolute Difference Recommender Evaluator metric
Date Wed, 26 Oct 2011 11:39:46 GMT
A-ha. I should elaborate then. The essence of the item-based algorithm
is estimating prefs as weighted averages of other prefs. The weights
are similarities. This depends on having prefs to average in the first
place in the data model. But it doesn't depend on whether the
similarity value uses ratings or not. Those weights are just weights,
wherever they come from.

The recommenders that operate without ratings don't actually compute a
weighted average anymore -- it doesn't make sense. They compute
something else to rank on, but it's no longer an estimate pref
actually. That's why AAD doesn't have real meaning there.

But in either case you're welcome to use log-likelihood similarity for
example which does not depend on pref values at all. It's just
supplying a value which is used as a weight in the first instance, and
something else in the second instance.

On Wed, Oct 26, 2011 at 12:35 PM, lee carroll
<lee.a.carroll@googlemail.com> wrote:
>> AAD is not valid for comparison when you're not using
>>ratings in your *recommender*. It's nothing to do with your similarity
>>metric.
>
> The penny drops.

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