Thank you very much, in this case, I will give the best relevant articles, but not really some new recommendations, in this case, I maybe should make some recommendations about new subjects for an user, with an User Based recommendation, and then make the same to retrieve linked articles.
That's it ? That's my first part, implicit recommendation, and for explicit recommendation, i have a user_feedback table, with : user_id, article_id, rating, so for that this is very common and not a problem. I'm wondering if I should choose a user based or an item basis engine for that, what is your advice ? Thanks! Ted Dunning wrote: > > Indeed not. But it *is* a case of the product architecture for > recommendations I was nattering about. > > The problem here is how to compute the (user x topic) x (item x topic)' > product efficiently. This can be done pretty well with either hadoop or > SQL. In Pig or native map-reduce, the trick is to group by the topic and > then group by (user, item), summing the results as you go. If either user > x > topic or item x topic is small then a map-side join is good for the first > group-by operation. If not, then doing two full-scale map-reduce > operations > is no big deal. > > You should consider how to weight different relevances, probably according > to overall frequency in the corpus. > > On Mon, Jun 22, 2009 at 1:32 PM, Sean Owen <[email protected]> wrote: > >> I see. This almost is not a 'classic' recommendation problem. If you >> have user-subject similarity, and subject-item similarity already, >> then user-item similarity is probably just the product of the two? so >> you can recommend items by ordering by similarity. >> > > -- View this message in context: http://www.nabble.com/Would-like-some-recommendation%2C-need-advice-tp24154572p24155217.html Sent from the Mahout User List mailing list archive at Nabble.com.
