Item based recommendations can also use more expensive off-line computations which can make recommendations more accurate. SVD based methods in particular can be very useful especially which smaller data sets.
On Wed, Oct 26, 2011 at 6:52 AM, Sean Owen <[email protected]> wrote: > Yes, I would still say so. You could still easily find this too slow > if you're using user-user similarities and there are a lot of users > and few items behind these 100M data points. Or vice versa. Past this > point it's almost certainly too slow; before this point it could also > be slow. You would tend to choose user-based if you have relatively > fewer users. I don't know if there's a hard-and-fast guideline there. > > On Wed, Oct 26, 2011 at 2:50 PM, Grant Ingersoll <[email protected]> > wrote: > > Sorry, should have been more clear. I was referring to if one is using a > user based recommender (e.g GenericUserBasedRecommender) vs. item based > recommender. Our general recommendation is that user based approaches won't > scale, I was wondering what the general cutoff is on a single machine, more > or less. Is it still 100M data points, roughly speaking? > > >
