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 :) >>> >> > >
