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From Xavier Rampino <xramp...@senscritique.com>
Subject Re: Question - Mahout Taste - User-Based Recommendations...
Date Tue, 29 Jan 2013 14:45:26 GMT
Just to add that you can also
use UserNeighborhood.getUserNeighborhood(userId) to find the most similar
users to a given one, should you want to.

On Tue, Jan 22, 2013 at 9:02 PM, Henning Kuich <hkuich@gmail.com> wrote:

> ok, thanks!
>
>
> On Tue, Jan 22, 2013 at 8:59 PM, Sean Owen <srowen@gmail.com> wrote:
>
> > That's a question of using item-item similarity. For that you need to
> > use something based on an ItemSimilarity, which is not user-based but
> > instead the item-based implementation. Or you can just use
> > ItemSimilarity directly to iterate over the possibilities and find
> > most similar, but, the recommender would do it for you.
> >
> > On Tue, Jan 22, 2013 at 7:50 PM, Henning Kuich <hkuich@gmail.com> wrote:
> > > Oh, I forgot one thing: Is it just as simple using the User-based
> > > recommendation to find similar products, or is this only possible using
> > > item-based recommendations? So basically if a given user rated a
> certain
> > > product with x stars, to figure out what item is most like the one he
> has
> > > just rated, but using only user-based recommendation algorithms?
> > >
> > > HK
> > >
> > >
> > > On Tue, Jan 22, 2013 at 7:44 PM, Henning Kuich <hkuich@gmail.com>
> wrote:
> > >
> > >> That's what i though. I just wanted to make sure!
> > >>
> > >> Thanks so much for the quick reply!
> > >>
> > >> HK
> > >>
> > >>
> > >>
> > >> On Tue, Jan 22, 2013 at 7:40 PM, Sean Owen <srowen@gmail.com> wrote:
> > >>
> > >>> Yes that's right. Look as UserBasedRecommender.mostSimilarUserIDs(),
> > >>> and Recommender.estimatePreference(). These do what you are
> interested
> > >>> in, and yes they are easy since they are just steps in the
> > >>> recommendation process anyway.
> > >>>
> > >>> On Tue, Jan 22, 2013 at 6:38 PM, Henning Kuich <hkuich@gmail.com>
> > wrote:
> > >>> > Dear All,
> > >>> >
> > >>> > I am wondering if I understand the User-based recommendation
> > algorithm
> > >>> > correctly.
> > >>> >
> > >>> > I need to be able to answer the following questions, given users
> and
> > >>> > ratings:
> > >>> >
> > >>> > 1) Which users are "closest" to a given user
> > >>> > and
> > >>> > 2) given a user and a product, predict the preference for the
> product
> > >>> >
> > >>> > apart from the standard "return topN" recommendations. But as
I
> > >>> understand
> > >>> > it, the above two questions are just "subquestions" of the topN
> > problem,
> > >>> > correct? Because the algorithm determines the "closest users"
since
> > >>> it's a
> > >>> > user-based recommender, and since it calculates all potential
> > user-item
> > >>> > associations, the second question should also be taken care of.
> > >>> >
> > >>> > Do I understand this correctly?
> > >>> >
> > >>> > I would greatly appreciate any help,
> > >>> >
> > >>> > Henning
> > >>> >
> > >>> >
> > >>> >
> > >>> >
> > >>> > Confidentiality Notice: This e-mail message, including any
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and
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> > >>>
> > >>
> > >>
> > >>
> > >> Confidentiality Notice: This e-mail message, including any
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> > >>
> > >
> > > Confidentiality Notice: This e-mail message, including any
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>
>
>
> --
>
> P. Henning J. L. Kuich
> email: hkuich@gmail.com
> twitter: @hkuich <http://twitter.com/hkuich>
> facebook: henning.kuich
> G+: hkuich
> Tel: +49 178 6065116
>
> Confidentiality Notice: This e-mail message, including any
> attachments, is for the sole use of the intended recipient(s) and may
> contain confidential and privileged information.  Any unauthorized
> review, use, disclosure or distribution is prohibited.  If you are not
> the intended recipient, please contact the sender by reply e-mail and
> destroy all copies of the original message.
>

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