On Thu, Dec 6, 2012 at 10:20 AM, Sean Owen <sro...@gmail.com> wrote: > Are you speaking specifically about the implementation in the .knn > package, which is a fairly particular thing, or just a "k nearest > neighbor" approaches in general? The latter aren't going away. > > kNN in general. Glad to hear it is not going away. I need a starting point to get up to speed. Thanks for clarifying.
> On Thu, Dec 6, 2012 at 3:18 PM, Koobas <koo...@gmail.com> wrote: > > 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 <sro...@gmail.com> 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 <s...@apache.org> > 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. > >> > > >> > > >> > > >> >
