thanks a lot!

On Tue, Jan 29, 2013 at 3:45 PM, Xavier Rampino
<[email protected]>wrote:

> 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 <[email protected]> wrote:
>
> > ok, thanks!
> >
> >
> > On Tue, Jan 22, 2013 at 8:59 PM, Sean Owen <[email protected]> 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 <[email protected]>
> 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 <[email protected]>
> > 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 <[email protected]>
> 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 <[email protected]>
> > > 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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