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 <ted.dunn...@gmail.com> wrote: On Fri, Feb 13, 2015 at 9:37 AM, Eugenio Tacchini < eugenio.tacch...@gmail.com> 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.
