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From Manuel Blechschmidt <>
Subject Re: Evalutation of recommenders
Date Wed, 11 Apr 2012 07:31:09 GMT
Hi Saikat,
I wrote my master thesis about evaluating recommender in real world examples:

So what you are  going to do is current research. This means that there are currently not
a lot of experiences.

In 2009 there was an online evaluation challenges which was part of ECML PKDD.

2009 ECML PKDD Discovery Challeng: Online Tag Recommendations. http://
Version: 2009, Checked: 2011-04-23

You will have to run all your recommenders in parallel to figure out which one is the best
one for optimizing business goals. I founded a company which is developing the described technology.
I am currently searching for a project starting in July 2012 where I can try this. So if you
are interested in hiring my feel free to send me a personal message.


On 10.04.2012, at 17:41, Saikat Kanjilal wrote:

> Hi everyone,We're looking at building out some clustering and classification algorithms
using mahout and one of the things we're also looking at doing is to build performance metrics
around each of these algorithms, as we go down the path of choosing the best model in an iterative
closed feedback loop (i.e. our business users manipulate weights for each attribute for our
feature vectors->we use these changes to regenerate an asynchronous model using the appropriate
clustering/classification algorithms and then replenish our online component with this newly
recalculated data for fresh recommendations).   So our end goal is to have a basket of algorithms
and use a set of performance metrics to pick and choose the right algorithm on the fly.  I
was wondering if anyone has done this type of analysis before and if so are there approaches
that have worked well and approaches that haven't when it comes to measuring the "quality"
of each of the recommendation algorithms.
> Regards  		 	   		  

Manuel Blechschmidt
CTO - Apaxo GmbH

Weinbergstr. 16
14469 Potsdam

Telefon +49 (0)6204 9180 593
Fax +49 (0)6204 9180 594
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Skype: Manuel_B86

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