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From Henning Kuich <hku...@gmail.com>
Subject Re: Question - Mahout Taste - User-Based Recommendations...
Date Tue, 22 Jan 2013 20:02:41 GMT
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
> >>> >
> >>> >
> >>> >
> >>> >
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> >>>
> >>
> >>
> >>
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> >>
> >
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-- 

P. Henning J. L. Kuich
email: hkuich@gmail.com
twitter: @hkuich <http://twitter.com/hkuich>
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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
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