The new Mahout recommender code doesn’t attempt to predict ratings at all. It tries to predict which items are of most interest to users. Ratings are a bit of a red herring.
If you want to learn about recommenders the old Mahout in Action text is out of date so I wouldn’t follow that as a learning tool. Again I’d point to the references at the top of this page, including an easy to read book by Ted. http://mahout.apache.org/users/recommender/intro-cooccurrence-spark.html This describes a recommender that can ingest most of a user’s clickstream, correlate user actions to some canonical action that you _do_ want to recommend (like watching a video) then makes the best ranking of recommended items. It’s multimodal because it can take actions like watching a video, rating a video, searching, being at some location, browsing categories, and turn the data into a prediction about what video the user is likely to want to watch. On Feb 16, 2015, at 1:32 AM, Eugenio Tacchini <[email protected]> wrote: 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. > >
