Hi all and thanks for your suggestions,
I am reading the content suggested and, for the "old" (non-spark) approach,
trying to see the various options from the javadoc here
https://builds.apache.org/job/Mahout-Quality/javadoc/ and the source code.

If I need to use a classical user-based technique, however, the only
alternative is the Taste-oriented code, am I right? Still, I can't see how
to perform a prediction for a a user/item couple, is there a class for that?

Thanks,

Eugenio


2015-02-12 17:54 GMT+01:00 Ted Dunning <ted.dunn...@gmail.com>:

> I would go so far as to say that all of the old Taste-oriented code is
> strongly deprecated.  The indicator-based approach that Pat refers to is
> the best way forward.
>
>
> On Thu, Feb 12, 2015 at 8:29 AM, Pat Ferrel <p...@occamsmachete.com> wrote:
>
> > The new cooccurrence recommender that works with a search engine has
> > several references at the top of the page here:
> > http://mahout.apache.org/users/recommender/intro-cooccurrence-spark.html
> >
> > On Feb 12, 2015, at 6:56 AM, John Hofmann <genghisu...@gmail.com> wrote:
> >
> > You're not missing any secret cache of mahout documentation as far as I
> > know.  I learned what the recommender options were by looking through the
> > source code.  They're spelled out there.  If you know any C-based
> languages
> > you can navigate the code pretty easily but expect to spend some time
> > getting familiar with the repo.
> >
> > The other avenue I've taken was the book "Mahout in Action."  It's a
> little
> > dated, but is generally still pretty applicable.  It goes into more
> detail
> > about why you'd pick one option over another.  I've noticed that most of
> > the blog posts about mahout assume you have a level of knowledge about
> > different ML algorithms that is comparable to what's in this book, so
> it's
> > a good one to read if you are going to be doing serious work with Mahout.
> >
> > There might be better options, but that's how I learned.  Hope this
> helps!
> >
> > On Thu, Feb 12, 2015 at 9:24 AM, Eugenio Tacchini <
> > eugenio.tacch...@gmail.com> wrote:
> >
> > > Hi all,
> > > I am new to mahout, it has been a couple of days now since I started
> > > working with it and I've found it very very powerful.
> > >
> > > I noticed, however, a general lack of documentation. I am working just
> > with
> > > the recommender system features and, correct me if I am wrong, I can't
> > find
> > > anywhere some complete documentation about. All I can find in the
> > official
> > > website is some quick-start tutorials.
> > >
> > > Knowing the theory about recommender systems, I expect a few very
> simple
> > > documentation pages which tell me something like:
> > >
> > > - The algorithms implemented are: 1) user-based, 2) item-based, 3)
> > > ..........
> > > - To use 1) in your code, follow these steps:
> > >  - choose a user similarity measures (available measures: 1) Pearson
> > > Correlation 2) Cosine similarity 3) ..... 4).......)
> > >  - choose a neighbors selection techniques (available techniques 1)
> > > threshold 2) fixed number 3).... 4)....)
> > > - compute the neighbors using the following
> > > UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1,
> > > similarity, dm);.......
> > >
> > > and so on until all the options available are covered.
> > >
> > > I didn't find anything like that, for example at the moment I am trying
> > to
> > > figure out how to compute a prediction (user-based algorithm), e.g.
> > predict
> > > the rating for user x movie y but I didn't find anything about that.
> > >
> > > Forgive me if there is something I am missing, I just want to focus on
> > the
> > > right content to learn about mahout, any suggestion is welcome.
> > >
> > > Thanks
> > >
> > > Regards,
> > >
> > > Eugenio Tacchini
> > >
> >
> >
>

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