I think so, but I cannot say that I know so.
On Mon, Jul 15, 2013 at 8:37 AM, Koobas <[email protected]> wrote: > Is a factorizing recommender a better idea for low volume data in general? > > > On Mon, Jul 15, 2013 at 11:35 AM, Ted Dunning <[email protected]> > wrote: > > > With such small data, this sounds (without thinking too much) like you > are > > doing reasonably well with LLR similarity. > > > > Have you tried a factorizing recommender? > > > > > > On Sun, Jul 14, 2013 at 10:49 PM, Jayesh <[email protected]> > > wrote: > > > > > 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 > > > > > >
