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 preferences for around 10.000 users it will take some seconds to generate recommendations for users with a lot of preferences. Normally what you do is just down sample the preferences that a certain user has to make it faster. When I understood LLR correctly than it is a smart way of doing this downsampling. Another approach would be using a model based approach: https://mahout.apache.org/users/recommender/matrix-factorization.html <https://mahout.apache.org/users/recommender/matrix-factorization.html> The following class contains an example: https://github.com/ManuelB/facebook-recommender-demo/blob/master/src/main/java/de/apaxo/bedcon/AnimalFoodRecommender.java <https://github.com/ManuelB/facebook-recommender-demo/blob/master/src/main/java/de/apaxo/bedcon/AnimalFoodRecommender.java> > > Is performance the only concern when using Taste or the algorithm's > implementation has also been improved in the current implementations > accessible via CLI. > > Thanks. /Manuel -- Manuel Blechschmidt Twitter: http://twitter.com/Manuel_B
