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 <hku...@gmail.com> wrote: > ok, thanks! > > > On Tue, Jan 22, 2013 at 8:59 PM, Sean Owen <sro...@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 <hku...@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 <hku...@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 <sro...@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 <hku...@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 > > >>> > 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. > > >>> > > >> > > >> > > >> > > >> 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. > > >> > > > > > > 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. > > > > > > -- > > P. Henning J. L. Kuich > email: hku...@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. >
