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.
