Hi Ted, Thanks for the reply. My training data could have around 100k users and around 1k items. The data is sparse (I have a boolean affinity - the user either bought the item or did not)
PS: I have been playing around with a sample code, using Loglikelihood Similarity to get a 24% precision, is this a par score? On Mon, Jul 15, 2013 at 10:58 AM, Ted Dunning <[email protected]> wrote: > Mahout will work fine for smaller data sizes. > > Collaborative filtering can be difficult in general with small data, > however. > > How many users and how many items? How many actions? > > > On Sun, Jul 14, 2013 at 10:22 PM, Jayesh <[email protected]> > wrote: > > > Hello, > > > > I am exploring the collaborative filtering algorithms in Mahout to build > a > > recommendation engine. > > > > I had recently gone for a Big Data conference where the speakers > suggested > > that using Mahout is overkill for anything that doesn't have some > terabytes > > of training data. > > > > I tried to google some cases on that, but no help, so turned to this > > thread. > > > > Would you suggest me using Mahout in my case when the training data might > > not be in terabytes, but some gigabytes, perhaps few 100s of megabytes? > > If not, do you suggest any other approach? > > > > Thank you. > > > > -- > > Best Regards, > > > > Jayesh > > > -- Best Regards, Jayesh
