Yes, I know I can implement a custom user similarity but what I want to do
is passing to mahout fixed, pre-computed user similarities I have already
stored in a text file in the easiest way possible, since I am not a Java
programmer.
If there is no way to do it, I will implement CustomUserSimilarity just by
reading the text file, storing the file in memory and returning the
corresponding similarity. I should do that making sure the read of the text
file is done just once, though.
Eugenio
2015-02-11 11:28 GMT+01:00 Juanjo Ramos <jjar...@gmail.com>:
> You can create your custom class with your similarity implementation. All
> you need is that class to implement the UserSimilarity interface and use it
> here
> UserSimilarity similarity = new PearsonCorrelationSimilarity(dm);
>
> instead of the PearsonCorrelationSimilarity.
>
> UserSimilarity similarity = new CustomUserSimilarity(dm); //
> CustomUserSimilarity
> implements UserSimilarity
>
> If the implementation of that CustomUserSimilarity is in C, you may want to
> look into JNI (Java Native Interface) to call C code from Java.
>
> Best,
> Juanjo.
>
> On Wed, Feb 11, 2015 at 9:48 AM, Eugenio Tacchini <
> eugenio.tacch...@gmail.com> wrote:
>
> > Hello Pat and thanks for your reply,
> > I know that when users >> items normally item-based works better and I
> > don't assume my similarity metric works better but I have, for research
> > purposes, to compare:
> >
> > - RMSE produced by a pearson correlation user-based algorithm VS
> > - RMSE produced by a user-based algorithm where similarities are computed
> > in a completely different and not standard way (algorithm implemented in
> C)
> >
> > so I am looking for a way to assign manually the user similarities; the
> > test will be performed just on a couple of datasets so it's fine if I
> have
> > to hard-code the assignment.
> >
> > Eugenio
> >
> >
> > 2015-02-10 23:58 GMT+01:00 Pat Ferrel <p...@occamsmachete.com>:
> >
> > > There are many algorithms in Mahout but not all are equal. Some
> > > combinations never perform well even though they are described in
> Mahout
> > in
> > > Action. The combination below is probably not the best.
> > >
> > > You seem to assume your user similarity metric is better than Mahout’s?
> > Do
> > > you have more users or items?
> > >
> > > If I were you I'd try user or item based recs in Mahout using LLR
> > > similarity. It’s always performed best when I’ve compared. I say this
> > > because I know of no way to do what you ask without writing some code
> and
> > > partly because I bet it will outperform.
> > >
> > > Also be aware that the only good way to compare completely different
> > > recommenders is A/B user testing.
> > >
> > > On Feb 10, 2015, at 3:39 AM, Eugenio Tacchini <
> > eugenio.tacch...@gmail.com>
> > > wrote:
> > >
> > > Hi all,
> > > I am new to mahout but I work with recommender systems, I have just
> tried
> > > to implement a simple user-based recommender:
> > >
> > > DataModel dm = new FileDataModel(new File("data/ratings.dat"));
> > >
> > > UserSimilarity similarity = new PearsonCorrelationSimilarity(dm);
> > >
> > > UserNeighborhood neighborhood = new
> > > ThresholdUserNeighborhood(0.1,similarity, dm);
> > >
> > > UserBasedRecommender r = new GenericUserBasedRecommender(dm,
> > neighborhood,
> > > similarity);
> > >
> > > I would like to compare the results of this recommender with another I
> > > implemented using another technology. The only differences between the
> > two
> > > algorithms is the way I choose neighbors; since I am not very fluent in
> > > Java, instead of implementing the second algorithm in mahout, I would
> > like
> > > to manually specify the neighbors for each user, is this possible?
> Which
> > is
> > > the easiest way to provide an alternative user-user similarity matrix
> > > (computed using my algorithm)?
> > >
> > > Just to recap: I want to use GenericUserBasedRecommender but providing
> an
> > > alternative users similarity matrix, without reimplementing my
> similarity
> > > algorithm in Java. Basically if I could import the similarities from a
> > text
> > > file it would be great, but other methods are fine as well.
> > >
> > > Thanks a lot in advance.
> > >
> > > Eugenio Tacchini
> > >
> > >
> >
>
