Hello, I was just referring to prediction in its classical meaning: you have a set of ratings (users rate items) and you want to predict one of the missing ratings e.g. P(User A, Item 23).
Eugenio 2015-02-13 21:04 GMT+01:00 Pat Ferrel <[email protected]>: > spark-rowsimilarity will give you a list of similar users (rows in the > interaction matrix) using LLR with several downsampling options. This works > with rows for input but you can input elements with a little custom code to > get exactly the same result. > > Let me understand the second part of your question. The recs query is > (user id, item id)? So you want both to contribute to the recommendations? > This is different than a typical “other people who like this also liked > these” type rec set, which is non-personal—the same for every user. > > If you are asking for something like recs on a product page using the item > being viewed as context and the user’s preference history too—the > multimodal recommender can do that. But please explain before I go into a > long reply. > > On Feb 13, 2015, at 9:53 AM, Ted Dunning <[email protected]> wrote: > > On Fri, Feb 13, 2015 at 9:37 AM, Eugenio Tacchini < > [email protected]> wrote: > > > If I need to use a classical user-based technique, however, the only > > alternative is the Taste-oriented code, am I right? > > > > Right. > > > > Still, I can't see how > > to perform a prediction for a a user/item couple, is there a class for > > that? > > > > Not directly, but I think that you cna cobble something simple together. > >
