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From web service <wbs...@gmail.com>
Subject Re: Help needed for Recommendation engine
Date Thu, 10 Nov 2011 17:40:40 GMT
Hi Vignesh,
   You might want to start with some of the examples that are bundled with
mahout. They are pretty straightforward.
I guess tweaking those to figure out how they work and adapting to your
needs might be easier. For ex. grouplens example.


On Thu, Nov 10, 2011 at 9:22 AM, bejoy ks <bejoyks@hotmail.com> wrote:

>
> Hey Vignesh
>          You can refer to the book Mahout in Action. It would help you
> with a deep knowledge on the available algorithms and choose the best one
> for your data set. If your user ids and item ids are numeric then
> distributed implementation of item based similarity is straight forward
> just use the packed jar avaliabe with mahout and provide the appropriate
> inputs. The IBM developer works article is really good. In addition you can
> refer the following as well
> https://cwiki.apache.org/MAHOUT/itembased-collaborative-filtering.html
>
> http://kickstarthadoop.blogspot.com/2011/05/mahout-recommendations-in-distributed.html
>
> Couple of points to take care
> - I'm not sure why you choose hive to store the final result. If you are
> looking at a distributed database within hadoop eco system it is not hive
> but hbase for all your low latency access. You need to go in for such a
> distributed database only if the recommendation results are too
> large,ranging to a few Tera bytes and not hence not scalable with
> traditional RDBMs. If the result size is manageable get it from hdfs and
> store into rdbms, which would be better choice for legacy/existing
> applications to consume.
> - On top, if your user ids or item ids in input data set are alphanumeric
> then it'd be better to have an input and output formatter wrapping your
> distributed recommender. It'd would inturn do the conversions between
> alphanumeric and numeric and vice versa on the data consumed and processed
> data by the recommender.
>
> Hope it helps!..
>
> Thanks and Regards
>        Bejoy.K.S
>
>
> > Date: Thu, 10 Nov 2011 15:16:11 +0100
> > Subject: Re: Help needed for Recommendation engine
> > From: pcoupet@gmail.com
> > To: user@mahout.apache.org
> >
> > This new article by Grant Ingersoll is really good to get started in my
> > opinion.
> >
> >
> http://www.ibm.com/developerworks/java/library/j-mahout-scaling/index.html?ca=drs-
> >
> > Pascal
> >
> >
> > 2011/11/10 VIGNESH PRAJAPATI <vignesh2066@gmail.com>
> >
> > > Ya ted,
> > > i am new in the mahout world,having java knowledge with hadoop ,mahout
> > > basics..
> > > i indeed want to develop recommender system with kmeans clustering
> > > algorithm.
> > > So it take little time for generating recommendation of items for
> > > millions of users based on their past activity like users rating on
> > > the perticular products and total number of product's page  visit by
> > > users.so,i have three input
> > > 1.userid
> > > 2.productid
> > > 3.rate or number of visits by userid provided at first no.
> > >
> > > And based on this will provide numbers of item or best item
> > > appropriate to user as recommeendation.and after all this information
> > > is stored in hadoop
> > >
> > > -----Original message-----
> > > From: Ted Dunning
> > > Sent:  10/11/2011, 7:09  pm
> > > To: user@mahout.apache.org
> > > Cc: user
> > > Subject: Re: Help needed for Recommendation engine
> > >
> > >
> > > Hive is not a database.
> > >
> > > You should test different algorithms. If you want suggestions you
> > > should say a lot more about what you are doing.
> > >
> > > Sent from my iPhone
> > >
> > > On Nov 10, 2011, at 5:39, VIGNESH PRAJAPATI <vignesh2066@gmail.com>
> wrote:
> > >
> > > > So how to start for it which mahout algorithm is sutable for this?
> > >
>
>

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