What Sean suggests is important. You may need to add down-sampling to the mix as well, but that is usually only necessary for speed, not quality of recommendations.
On Mon, Dec 26, 2011 at 2:07 PM, Sean Owen <[email protected]> wrote: > What item similarity metric are you using? Log-likelihood tends to > account for an item's baseline popularity and normalize it away. So a > best-seller isn't similar to an item just because it's a best-seller > and shows up a lot, but because it shows up an unusually large number > of times, even granting it's a best seller. Try that if you're not > already using it. > > On Mon, Dec 26, 2011 at 4:01 PM, Valentin Pletzer <[email protected]> > wrote: > > Hi, > > > > I am trying to achieve some item-to-item-recommendations and the setup > > works quite well. But one thing I stumbled across is that some items are > so > > popular that they are a recommendation for nearly every other item. In > the > > Amazon paper they say that they are sampling the bestseller buying > > customers. Do I have to do this preprocessing step myself or does Mahout > > help with that? > > > > Thanks > > Valentin >
