Re: Own recommender

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

Re: Own recommender

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

Re: Own recommender

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

Re: Own recommender

- 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

Re: Own recommender

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

Re: Own recommender

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

Re: Own recommender

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

Re: Own recommender

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

RE: Own recommender

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

Re: Own recommender

. 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

Re: Own recommender

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 :)

Own recommender

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 :)