As a n00b, I am still revolving in the kNN space. Could you please point me to some details on ALS. Thanks!
On Thu, Dec 6, 2012 at 10:14 AM, Sean Owen <[email protected]> wrote: > The tree-based ones are very old and not fast, and were more of an > experiment. I recall a few questions about them but it seemed like > people were really just trying to do clustering, and this is a bad way > to do clustering. > > knn is old too, and in a sense spiritually quite similar to ALS. I > don't mind removing it either. > > It would seal it if there were even a nominal argument that this > improves the rest of the code base -- less to maintain, removes > duplication, inconsistency, etc. I could imagine that argument here. > > On Thu, Dec 6, 2012 at 3:06 PM, Sebastian Schelter <[email protected]> wrote: > > Hi there, > > > > I'm currently thinking whether we should do a little cleanup in the > > non-distributed recommenders package and throw out recommenders that > > have not been used/asked about on the mailinglist or that have been > > replaced by a superior implementation. > > > > If anyone reads this and sees a recommender, he/she wants to be kept, > > please shout! > > > > /s > > > > Here's a list of suggested stuff to remove, let me know what you think: > > > > org.apache.mahout.cf.taste.impl.recommender.svd.FunkSVDFactorizer > > > > RatingSGDFactorizer should be learning faster and has a nicer model as > > it includes user/item biases > > > > > > > org.apache.mahout.cf.taste.impl.recommender.svd.ImplicitLinearRegressionFactorizer > > > > Seems to be using the same model as ALSWRFactorizer, however there are > > no tests and ALSWR can handle more explicit and implicit feedback > > > > > > org.apache.mahout.cf.taste.impl.recommender.TreeClusteringRecommender > > org.apache.mahout.cf.taste.impl.recommender.TreeClusteringRecommender2 > > org.apache.mahout.cf.taste.impl.recommender.knn > > > > I don't recall anybody using those or asking about them the last years. > > > > > > >
