Can we step back a bit, is speed of query the only issue? Why do you care how
long it takes? This is example data, not yours. Some of the techniques you
mention below are Hadoop mapreduce based approaches. These by their nature are
batch oriented. The mapreduce item-based recommender may take
Truer words than this were never said.
Sent from my iPhone
On May 9, 2014, at 8:36, Pat Ferrel pat.fer...@gmail.com wrote:
let your data determine this, not example data.
Hi there,
I mentioned a problem of using the ItemBasedRecommender. It is so much slower
then using UserBasedRecommender.
@Sebastian: You said limiting the precomputation file should work. For example:
only 50 similarities for an Item. You also said this feature is not included
in the
(Resending mail without sending my digital signature)
Hi there,
I mentioned a problem of using the ItemBasedRecommender. It is so much slower
then using UserBasedRecommender.
@Sebastian: You said limiting the precomputation file should work. For example:
only 50 similarities for an Item.
You can always run Hadoop in a local mode. Nothing prevents a single node
from being a cluster. :-)
On Thu, Apr 17, 2014 at 7:43 AM, Najum Ali naju...@googlemail.com wrote:
Ted,
Is it also possible to use ItemSimilarityJob in a non-distributed
environment?
Am 17.04.2014 um 16:22 schrieb
You can, but you shouldn't :)
On 04/18/2014 07:23 PM, Ted Dunning wrote:
You can always run Hadoop in a local mode. Nothing prevents a single node
from being a cluster. :-)
On Thu, Apr 17, 2014 at 7:43 AM, Najum Ali naju...@googlemail.com wrote:
Ted,
Is it also possible to use
Shouldn't, yes.
But for a toy dataset, it might work out.
On Fri, Apr 18, 2014 at 10:25 AM, Sebastian Schelter
ssc.o...@googlemail.com wrote:
You can, but you shouldn't :)
On 04/18/2014 07:23 PM, Ted Dunning wrote:
You can always run Hadoop in a local mode. Nothing prevents a single
Could you take the output of the precomputation, feed it into a
standalone recommender and test it there?
On 04/17/2014 11:37 AM, Najum Ali wrote:
@sebastian
Are you sure that the precomputation is done only once and not in every request?
Yes, a @Bean annotated Object is in Spring per
@Sebastian
What do u mean with a standalone recommender? A simple offline java main
program?
Am 17.04.2014 um 11:41 schrieb Sebastian Schelter s...@apache.org:
Could you take the output of the precomputation, feed it into a standalone
recommender and test it there?
On 04/17/2014 11:37
Yes, just to make sure the problem is in the mahout code and not in the
surrounding environment.
On 04/17/2014 11:43 AM, Najum Ali wrote:
@Sebastian
What do u mean with a standalone recommender? A simple offline java main
program?
Am 17.04.2014 um 11:41 schrieb Sebastian Schelter
Ok, here you go:I have created a simple class with main-method (no server and other stuff):public class RecommenderTest { public static void main(String[] args) throws IOException, TasteException { DataModel dataModel = new FileDataModel(new
@Sebastian
wow … you are right. The original csv file is about 21mb and the corresponding
precomputed item-item similarity file is about 260mb!!
And yes, there are wide more than 50 most similar items“ for an item ..
Trying to restrict this to 50 (or something like that) most similar items for
Ted,
Is it also possible to use ItemSimilarityJob in a non-distributed environment?
Am 17.04.2014 um 16:22 schrieb Ted Dunning ted.dunn...@gmail.com:
Najum,
You should also be able to use the ItemSimilarityJob to compute a limited
indicator set.
This is stepping off of the path you have
13 matches
Mail list logo
