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From Koobas <koo...@gmail.com>
Subject Re: Boolean preferences and evaluation
Date Fri, 25 Jan 2013 14:30:47 GMT
Great suggestion!
Will do.


On Fri, Jan 25, 2013 at 1:10 AM, Sean Owen <srowen@gmail.com> wrote:

> Why not test both the original and pruned data set? The low-rating
> data may still help, even when the rating is forgotten.
> I would not base the decision just on whether you can make
> recommendations to N users but the quality of recommendations overall.
>
> In this particular data set, which is rich and un-noisy, the ratings
> are probably valuable information and I imagine you will do better
> with any approach that doesn't drop them.
>
> On Fri, Jan 25, 2013 at 2:19 AM, Koobas <koobas@gmail.com> wrote:
> > They use a boolean recommender on the 10M MovieLens data
> > with negative ratings removed (including only 3 stars or more).
> > I wonder if this is a valid approach, as opposed to not removing
> anything.
> >
> > I actually went through the exercise of removing negative ratings from
> the
> > 10M MovieLens set,
> > and made the following observations:
> >
> > - It removes about 17% of all ratings,
> > - 15 users disappear (out of 70,000),
> > - 79 movies disappear (out of 10,000).
> >
> > So, it does not seem to hurt the overall exercise.
> > Reasonably small fraction of ratings is gone.
> > We will not recommend movies to a dozen users, who did not line anything.
> > We will not be recommending movies which nobody liked.
> >
> > I would definitely appreciate some comments about that approach.
>

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