49 seconds is orders of magnitude too long -- something is very wrong here, for so little data. Are you running this off a database? or are you somehow counting the overhead of 3-4K network calls?
On Mon, Apr 22, 2013 at 11:22 PM, Gabor Bernat <ber...@primeranks.net> wrote: > Hello, > > I'm using Mahout in a system, where the typical response time should be > below 100ms. I'm using an item based recommender with float preference > values (with Tanimato similarity for now, which is passed into a > CachingItemSimilarity objec for performance reasonst). My model has around > 7k items, 26k users with around 100k preferences linking them. > > Instead of performing a recommendation, I only need to estimate preferences > of the user for around 3-4k items (this is important, as this allows the > integration of a business rule engine in the recommendation process inside > the system where I'm working). > > Now my problem is that for users with lots of preferences (200+) this > estimation process takes forever (49second+). I'm assuming the issue lies > into the calculation of the similarity measurements; so I though I'll do > this asynchroniously in a train like process, save it, and at start up just > load it into memory this precomputed information. However, I cannot see any > way to load this information into the CachingSimilarity object; nor can I > persist the CachingSimilarity object and load it. > > So any ideas, on how to cut down the estimation times? > > Thanks, > > Bernát GÁBOR
