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Date: Thu, 28 Oct 2010 12:05:52 +0100
Message-ID: <AANLkTi=EfyVNTJSa+ORvQ-rGEQhuRzHaObrd-ShG2vVZ@mail.gmail.com>
Subject: Re: Ease of recommendation for a user
From: Steven Bourke <sbourke@gmail.com>
To: user@mahout.apache.org
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It's always worth considering that a model does not reflect the true nature
of your recommendation. You can't really measure good serendipity or
diversity. You could possibly look at the quality of the users neighborhood
when generating the recommendation. i.e is their a good level of similarity
between the users or is it quite low. If its quite low, in theory the
recommendations are probably not going to be great given the sparsity of
overlap.

Couple of good starting points here on scholar.
http://scholar.google.com/scholar?hl=en&client=safari&rls=en&q=recommendation+sparse+data&um=1&ie=UTF-8&sa=N&tab=ws

On Thu, Oct 28, 2010 at 9:23 AM, Sean Owen <srowen@gmail.com> wrote:

> What's your intuition -- what would you do with this figure? Users with
> higher variance are easier or harder to recommend well for? I don't know if
> that directly affects the quality... probably the diversity of quantity of
> prefs is more directly relevant.
>
> On Thu, Oct 28, 2010 at 7:31 AM, Lance Norskog <goksron@gmail.com> wrote:
>
> > The following code is an attempt to decide how easy it to give a
> > recommendation to a given user. Does the user give us enough to go on?
> >
> > The idea is to get recommendations for a user and measure their standard
> > deviation. As a separate task, get the raw preferences value from the
> data
> > model for that recommendation, if available, and measure their standard
> > deviation also. I'm not sure this is the right approach.
> >
> > Are there standard models for this question?
> >
> >
>

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