Segment the users into demographics or some other classification system? Everyone thinks they're unique like snowflakes, but, really we're not.
On Sun, Apr 15, 2012 at 3:59 PM, Will Chiong <[email protected]> wrote: > 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? > -- Lance Norskog [email protected]
