Thanks Sebastian.
This is the code I used for Mahout-0.3 and Mahout-0.5:
int N = 1000;
long totalTime = 0L;
int n = 0;
Recommender rec = null;
final DataModel train = new FileDataModel(new File(trainFile));
ItemSimilarity sim = new PearsonCorrelationSimilarity(train);
sim = new CachingItemSimilarity(sim, train);
rec = new GenericItemBasedRecommender(train, sim);
LongPrimitiveIterator users = train.getUserIDs();
while (users.hasNext()) {
long u = users.nextLong();
n++;
long time = System.currentTimeMillis();
rec.recommend(u, N);
time = System.currentTimeMillis() - time;
totalTime += time;
}
System.out.println("Average time - using cache " + strategy + "
(ms): " + (1.0 * totalTime / n));
In Mahout-0.3, the average time per user was 310.99 ms, and for 0.5 it
was 649.55 ms.
As I said in my previous mail, here I was only concerned on measuring
the time performance of recommenders.
Thanks,
Alejandro
Sebastian Schelter escribió:
Hi Alejandro,
you have to provide a detailed description of your benchmark.
There is no such thing is "the efficiency" of a recommender. Mahout
offers a wide variety of implementations and components that can be
glued together to form a recommender. There are a lot of knobs to adjust
that offer trade-offs between execution time, scalability, quality and
recency.
--sebastian
On 15.12.2011 09:30, Alejandro Bellogin Kouki wrote:
Hi all,
some months ago I performed some efficiency comparisons between the
execution times of one implementation of mine and user- and item-based
CF recommenders in Mahout. By that time, I was using Mahout-0.3 and I
obtained some decent values, taking into account that I was measuring
the average recommendation time per user (that is, how long it took to
execute the method 'recommend(u, N)').
Yesterday, I decided to make the same tests with the latest stable
version (Mahout-0.5), and, to my suprise, the execution times in the
latest implementation have been multiplied by a factor of 2.
Do you have any idea of why is that happening? Is it documented
anywhere? I have specific numbers and code in case you want to check
them out.
I want to emphasize that I am measuring how much time the recommender
spends on generating a ranking, and, probably, if you have changed how
the plausible items to be recommended for a user are obtained, then this
change may have caused this situation.
Thank you in advance.
Best regards,
Alejandro
--
Alejandro Bellogin Kouki
http://rincon.uam.es/dir?cw=435275268554687
