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? I was wondering if there is a library with the latest implementation that can be used in a Java or Scala project?
Best. On Mon, Jan 19, 2015 at 9:51 PM, Pat Ferrel <p...@occamsmachete.com> wrote: > I guess I’ll put a page on the mahout site. For now some references: > > small free book here, which talks about the general idea: > https://www.mapr.com/practical-machine-learning > preso, which talks about mixing actions or other indicators: > http://occamsmachete.com/ml/2014/10/07/creating-a-unified-recommender-with-mahout-and-a-search-engine/ > two blog posts: > http://occamsmachete.com/ml/2014/08/11/mahout-on-spark-whats-new-in-recommenders/http://occamsmachete.com/ml/2014/09/09/mahout-on-spark-whats-new-in-recommenders-part-2/ > mahout docs: > http://mahout.apache.org/users/recommender/intro-cooccurrence-spark.html > > > On Jan 19, 2015, at 3:02 AM, Juanjo Ramos <jjar...@gmail.com> wrote: > > Hi Pat, > Do you know if there is any tutorial for the Scala recommender code? > Mahout's site keeps pointing here: > http://mahout.apache.org/users/recommender/userbased-5-minutes.html > > Thanks. > > On Sat, Jan 17, 2015 at 4:24 PM, Pat Ferrel <p...@occamsmachete.com> wrote: > > > The newest recommender code runs on the new Scala R-like DSL. It is > > cooccurrence based and supports only LLR. LLR is used to downsample > > cooccurrences comparing all pairs of items. I’ve done fairly careful > > offline testing of all the similarity methods of Mahout’s hadoop and > > in-memory recommenders and LLR was a clear winner. > > > > However if you have something new you want to try, look at the Scala > > SimilarityAnalysis class. For runtime efficiency it first calculates > > cooccurrences by performing [AA’] then calculating LLR on elements by row > > and downsampling in one step. You could look at some other similarity > > method for downsampling there. > > > > On Jan 16, 2015, at 12:44 AM, ARROYO MANCEBO David < > > david.arr...@altran.com> wrote: > > > > 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. > > > > > > > > On Thu, Jan 15, 2015 at 7:10 AM, Juanjo Ramos <jjar...@gmail.com> wrote: > > > >> Hi David, > >> You implement your custom algorithm and create your own class that > >> implements the UserSimilarity 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 in apache mahout > >>> with my personal algorithm? I need a custom UserSimilarity. Maybe a > >>> subclass from UserSimilarity like PearsonCorrelationSimilarity? > >>> > >>> Thanks > >>> Regards :) > >>> > >> > > > > > >
