Hi all,
Apologies if this is a repeat question as I just joined the list but I have
a question about the way that metrics like Cosine and Pearson are
calculated in Hadoop "mode" (i.e. non Taste).
As far as I understand, the vectors used for computing pairwise item
similarity in Taste are based on
I haven't tried Mahout on Windows in a while but the eclipse part should
just be able to be done through using mvn eclipse:eclipse and import the
project(s) into your workspace.
Then you should be able to use the LocalJobRunner to do some basic testing
in Eclipse.
Cheers
Amit
On Tue, Nov 26, 20
Thanks Sebastian! Is there a particular reason for that?
On Nov 27, 2013 7:47 AM, "Sebastian Schelter"
wrote:
> Hi Amit,
>
> You are right, the non-corated items are not filtered out in the
> distributed implementation.
>
> --sebastian
>
>
> On 26.11.2013
arity between two
vectors of different length which essentially is going on here I think?
Thanks again
Amit
On Nov 27, 2013 9:06 AM, "Sebastian Schelter"
wrote:
> Yes, it is due to the parallel algorithm which only looks at co-ratings
> from a given user.
>
>
> On 27.11.
test.
>
> The distributed code doesn't look at vectors of different lengths, but
> simply assumes non-existent ratings as zero.
>
> --sebastian
>
> On 27.11.2013 16:09, Amit Nithian wrote:
> > Comparing this against the non distributed (taste) gives different
> answe
r it's very
possible I am completely misunderstanding something :-).
Thanks again!
Amit
On Wed, Nov 27, 2013 at 8:23 AM, Amit Nithian wrote:
> Hey Sebastian,
>
> Thanks again. Actually I'm glad that I am talking to you as it's your
> paper and presentation I have quest
e and the expression m_x e . y can be computed
> (at lease in Mahout) in map-reduce idiom as
>
> y.aggregate(Functions.PLUS, Functions.mult(m_x))
>
>
>
>
> On Fri, Nov 29, 2013 at 9:31 PM, Amit Nithian wrote:
>
> > Okay so I rethought my question and realized
orm(1) / vector.getNumNonZeroElements();
Which looks like it's taking the sum and dividing by the number of defined
elements. Which would make my [5 - 4] average be 4.5.
Thanks again
Amit
On Fri, Nov 29, 2013 at 10:34 PM, Ted Dunning wrote:
> On Fri, Nov 29, 2013 at 10:16 PM, Amit Nithian wrote:
>
> >
Thanks guys! So the real question is not so much what's the average of the
vector with the missing rating (although yes that was a question) but
what's the average of the vector with all the ratings specified but the
second rating that is not shared with the first user:
[5 - 4] vs [4 5 2].
If we a
When you say without hadoop does that include local mode? You can run these
examples in local mode that doesn't require a cluster for testing and
poking around. Everything then runs in a single jvm.
On Dec 1, 2013 9:18 PM, "Shan Lu" wrote:
> Hi,
>
> I am working on a very simple k-means clusterin
glikelihood ratio test
> give much better results. The current implementation of Mahout's
> distributed itembased recommender is clearly designed and tuned for the
> latter usecase.
>
> I hope that answers your question.
>
> --sebastian
>
> On 01.12.2013 18:10, Amit Nithian w
11 matches
Mail list logo
