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From Sean Owen <sro...@gmail.com>
Subject Re: Two learning competitions that might be of interest for Mahout
Date Tue, 15 Feb 2011 11:57:19 GMT
If I may guess at the answer --

Yes in theory it would be better to score output on the quality of its
top recommendations, rather than on accuracy of predicted ratings,
which are just one means to that goal. There are of course contexts
where you have no ratings, so the winning technique here may not
translate to those scenarios.

Perhaps output would be scored on what proportion of the top k match
the real top k preferred items. And so the test would actually
withhold the top k rated items and ask recommenders to predict them.
This has two problems I can see, however.

The small problem is that chopping off the top ratings makes the test
data systematically different than real data. There's a lot of
"information" in those top ratings versus any arbitrary k.

The bigger problem is that the user's top k ratings are not
necessarily the same as the best k recommendations! Let's say I've
never seen the movie Breathless, but, if I do, I'll find it's actually
my favorite movie ever. A recommender would be right in making this a
top recommendation. But a recommender evaluation framework such as
this contest might use can't know that, so would count that "wrong".

Evaluating rating accuracy is at least unambiguous in comparison and
so can form the basis of a competition.
And to be fair, most people making production recommender systems
would expect it to be able to estimate a rating, in addition to making
recommendations.




On Tue, Feb 15, 2011 at 11:19 AM, Chen_1st <y.c@live.cn> wrote:
> Hi, Markus,
>
> I am curious why the competition still tries to predict the rating
> values, now that top k recommendation is more practical in real life
> applications, and it's illustrated by many papers that rating value
> prediction is not so useful for discovery of top k items.
>
> Best Regards.
>
> Chen

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