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From Koobas <koo...@gmail.com>
Subject Re: Remove unused recommenders?
Date Thu, 06 Dec 2012 15:22:40 GMT
On Thu, Dec 6, 2012 at 10:20 AM, Sean Owen <srowen@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 <koobas@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 <srowen@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 <ssc@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.
> >> >
> >> >
> >> >
> >>
>

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