Sorry for late response on this. Might be worth checking out: https://github.com/rawkintrevo/fsf17-twitter-recos
This is the corresponding talk. (relevant part starts at about 18:30) https://youtu.be/h3j1JdtbhOI Trevor Grant Data Scientist https://github.com/rawkintrevo http://stackexchange.com/users/3002022/rawkintrevo http://trevorgrant.org *"Fortunate is he, who is able to know the causes of things." -Virgil* On Thu, May 25, 2017 at 9:51 AM, Alessandro Dias <alessandrosd...@gmail.com> wrote: > Hi, > > I learned in this site below how to use ALS facorization algoritm to made > recommendations in Mahout Framework. > > https://mahout.apache.org/users/recommender/intro-als-hadoop.html > > From this: > - we inform a file with the rating (user, item, rating), in my case I have > implicit ratings; > - then get the files of the two latent matrices generated; and > - finally we insert theses files in a recommender engine that generate a > file with the list of recomendations for each user. > > > I think that it is made for big e-commerce companies periodically. (the > model and recomendations is built periodically in an offline moments) > > > > At my case, I'm going to do an online experiment of recommender. This model > recommender will be the control group. > > I have a file with ratings of a set of old users and I will have a set of > new users on this online experiment. The old users will not participate > this experiment. > > Theses new users will use the recommener system for 2 weeks in the online > experiment. > > > > >> How to use ALSWRFactorizer recommender (non-hadoop) from Mahout in > online experiments ? > > I'd like to build a model once and use it to the new users... > > >> Will I have to run the algoritm (re-buid the model) in each > recomendation made during the online experiment ? > > Thanks and Regards, > > Alessandro Dias >