This is on a 2-core machine, running 2 threads of load. It's a MacBook Pro, dual 3 GHz Intel Core Duos I think (64-bit). I allow it 2GB of heap though usage is about 1GB or so depending on the algorithm. I'm trying a lot of variants but, for example, a simple user-based recommender with Euclidean distance similarity and nearest-2 neighborhood recommends in about 100ms per user.
I strongly suspect the difference is this translation. You have a much smaller set, simpler algorithms, and beefier hardware. On Tue, Nov 24, 2009 at 8:37 PM, Otis Gospodnetic <[email protected]> wrote: > Hi, > >> Yes, that's quite small. As a reference. I'm currently writing up a >> case study on a data set with 130K users and 160K items and >> recommendation time is from 10ms to 200ms, depending on the algorithm. > > At what load/concurrency, on what type of hardware, with how large of a heap > and with what similarity & friends? > > Thanks, > Otis
