Ah, I see... I tried this and unfortunately the recommendations are extremely slow when I invert the data model.
I have about 2 million users, and 9000 items. The normal recommendations I did before (recommending items for users) takes only seconds. When I tried your suggestion to suggest an audience of users for an item, a recommend call took over an hour. Are there any suggestions for improving the speed of recommendations, or specific recommenders to use for this kind of dataset? Thanks again, -Will On Apr 14, 2012, at 3:38 AM, Burak Arikan wrote: > In other words, turning your "UserID, ItemID, rank" list to a "ItemID, > UserID, rank" list will generate user recommendations to items. > > Cheers, > burak > @arikan > > On Apr 14, 2012, at 10:32 AM, Burak Arikan <[email protected]> wrote: > >> Replace the userIDs with itemIDs in your csv data, that will do it. >> >> Cheers, >> burak >> @arikan >> >> On Apr 14, 2012, at 8:17 AM, Will C <[email protected]> wrote: >> >>> So I've seen methods to have Mahout Taste recommend items for a user, such >>> as: >>> https://builds.apache.org/job/Mahout-Quality/javadoc/org/apache/mahout/cf/taste/recommender/Recommender.html#recommend(long, >>> int) >>> >>> Is there the equivalent for the opposite, where I want to find a set of >>> users that can be recommended a product?
