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From Grant Ingersoll <gsing...@apache.org>
Subject Re: mahout for news recommendation?
Date Wed, 01 Apr 2009 16:11:16 GMT
Do a "mvn install" from the top level directory first: http://lucene.apache.org/mahout/taste.html#demo

HTH,
Grant

On Apr 1, 2009, at 11:35 AM, Joshua Bronson wrote:

> Thanks all for the good info. Taste definitely sounds like a promising
> direction for us to go in for our recommendation service.
> I'm working through the installation of the GroupLens demo, but the  
> mvn
> package step is failing with the output at
> http://paste.pocoo.org/show/110618/. Haven't looked into this yet,  
> just
> thought I'd post to the list first with my progress. If anyone else  
> uses
> IRC, I've created (and am currently the only one in) the #mahout  
> channel on
> freenode. Hope to see some of you in there!
>
> Josh
>
> On Wed, Apr 1, 2009 at 5:48 AM, Sean Owen <srowen@gmail.com> wrote:
>
>> Couple clarifications -
>>
>> The CF components are oriented to on-line, real-time use, though of  
>> course
>> one can trivially build a batch job out of that. That is what I did  
>> with
>> the
>> EC2 image that cranks out recommendations for all users.
>>
>> The CF component is also already parallelized as much as is  
>> practical.
>> There
>> are already Hadoop jobs for parallel, batch operation.
>>
>> Finally if you have some external notion of item similarity, like  
>> text
>> similarity between articles, you can and should include this info by
>> creating an ItemSimilarity with this knowledge. In that case you  
>> want to
>> use
>> an item-based recommender, since it is only in such a case that  
>> item-based
>> recommenders have a distinct advantage.
>>
>> On Apr 1, 2009 10:32 AM, "Otis Gospodnetic" <otis_gospodnetic@yahoo.com 
>> >
>> wrote:
>>
>>
>> it's the former.  Taste is still not parallelized, but other parts of
>> Mahout
>> are, and they make use of Hadoop.
>>

--------------------------
Grant Ingersoll
http://www.lucidimagination.com/

Search the Lucene ecosystem (Lucene/Solr/Nutch/Mahout/Tika/Droids)  
using Solr/Lucene:
http://www.lucidimagination.com/search


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