spark-itemsimilarity uses SimialrityAnalysis,cooccurrence, which does the
majority of the work. Examples of how to use it and handle I/O are in the CLI
driver mahout/spark/src/…/drivers/ItemSimilarityDriver.scala. All of this is
available as a library. The nature of Spark makes creating
Juanjo,
Using the Taste components, it will be almost impossible to get really high
performance. For that, using the itemsimilarity program to feed a search
index is the best alternative.
The scala version of the itemsimilarity program is available in Scala and
could be called fairly easily as
Hi Juan,
On 21.01.2015, at 11:05, Juanjo Ramos jjar...@gmail.com wrote:
Thanks Pat for the resources.
Please correct me if I'm wrong but all Mahout's latest tools are command
line tools only, is that correct?
Yes, this is kind of correct. All tools are command line based. There was some
-
De: Ted Dunning [mailto:ted.dunn...@gmail.com]
Enviado el: jueves, 15 de enero de 2015 20:05
Para: user@mahout.apache.org
Asunto: Re: Own recommender
The old Taste code is not the state of the art. User-based recommenders
built on that will be slow.
On Thu, Jan 15, 2015 at 7
Hi Juanjo,
On 21.01.2015, at 11:20, Juanjo Ramos jjar...@gmail.com wrote:
Hi Manuel,
Thanks for the update.
I'm using Mahout in a simple Java application myself. Following Ted's
comment a few posts back, I was just concerned about the performance.
So if you have more than around 300.000
Hi Manuel,
Thanks for the update.
I'm using Mahout in a simple Java application myself. Following Ted's
comment a few posts back, I was just concerned about the performance.
Is performance the only concern when using Taste or the algorithm's
implementation has also been improved in the current
el: jueves, 15 de enero de 2015 20:05
Para: user@mahout.apache.org
Asunto: Re: Own recommender
The old Taste code is not the state of the art. User-based recommenders
built on that will be slow.
On Thu, Jan 15, 2015 at 7:10 AM, Juanjo Ramos jjar...@gmail.com wrote:
Hi David,
You
interface.
When you then instantiate your User-Based recommender, just pass your
custom class for the UserSimilarity parameter.
Best.
On Thu, Jan 15, 2015 at 1:11 PM, ARROYO MANCEBO David
david.arr...@altran.com wrote:
Hi folks,
How I can start to build my own recommender system
Any idea, Ted? :)
-Mensaje original-
De: Ted Dunning [mailto:ted.dunn...@gmail.com]
Enviado el: jueves, 15 de enero de 2015 20:05
Para: user@mahout.apache.org
Asunto: Re: Own recommender
The old Taste code is not the state of the art. User-based recommenders built
on that will be slow
.
When you then instantiate your User-Based recommender, just pass your
custom class for the UserSimilarity parameter.
Best.
On Thu, Jan 15, 2015 at 1:11 PM, ARROYO MANCEBO David
david.arr...@altran.com wrote:
Hi folks,
How I can start to build my own recommender system in apache mahout
david.arr...@altran.com wrote:
Hi folks,
How I can start to build my own recommender system in apache mahout with
my personal algorithm? I need a custom UserSimilarity. Maybe a subclass
from UserSimilarity like PearsonCorrelationSimilarity?
Thanks
Regards :)
Hi folks,
How I can start to build my own recommender system in apache mahout with my
personal algorithm? I need a custom UserSimilarity. Maybe a subclass from
UserSimilarity like PearsonCorrelationSimilarity?
Thanks
Regards :)
12 matches
Mail list logo