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From Dominik Hübner <>
Subject Re: Setting up a recommender
Date Fri, 19 Jul 2013 20:06:44 GMT
+1 for getting something like that in a future release of Mahout

On Jul 19, 2013, at 10:02 PM, Sebastian Schelter <> wrote:

> It would be awesome if we could get a nice, easily deployable
> implementation of that approach into Mahout before 1.0
> 2013/7/19 Ted Dunning <>
>> My current advice is to use Hadoop (if necessary) to build a sparse
>> item-item matrix based on each kind of behavior you have and then drop
>> those similarities into a search engine to deliver the actual
>> recommendations.  This allows lots of flexibility in terms of which kinds
>> of inputs you use for the recommendation and lets you blend recommendations
>> with search and geo-location.
>> On Fri, Jul 19, 2013 at 12:33 PM, Helder Martins <
>>> wrote:
>>> Hi,
>>> I'm a dev working for a web portal in Brazil and I'm particularly
>>> interested in building a item-based collaborative filtering recommender
>>> for our database of news articles.
>>> After some coding, I was able to get some recommendations using a
>>> GenericItemBasedRecommender, a CassandraDataModel and some custom
>>> classes that store item similarities and migrated item IDs into
>>> Cassandra. But know I'm in doubt of what is normally done with this
>>> recommender: Should I run this as a daemon, cache the recommendations
>>> into memory and set up a web service to consult it online? Should I pre
>>> process these recommendations for each recent user and store it
>>> somewhere? My first idea was storing all these recs back into Cassandra,
>>> but looking into some classes it seems to me that the norm is to read
>>> the input data and store the output always using files. Is this a common
>>> practice that benefits from HDFS?
>>> My use case here is something around 70k recommendations requests per
>>> second.
>>> Thanks in advance,
>>> --
>>> Atenciosamente
>>> Helder Martins
>>> Arquitetura do Portal e Sistemas de Backend
>>> +55 (51) 3284-4475
>>> Terra
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