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From Sean Owen <>
Subject Re: Which is more effective?
Date Tue, 21 Jun 2011 22:50:08 GMT
For #1 -- there is still an unanswered issue in there, and that's how you
extract features from items. I assume you already have some scheme for that.
(The framework can't quite help you there.)

But then the question remains how you compute similarity from features. So
#1 isn't a concrete possibility by itself.

I am not sure if a classifier can be used very directly to figure a notion
of item-item similarity. I am sure it can be used in some sense, but it
doesn't seem like the most direct tool. A simpler notion, of similarity or
distance, is what you want, and that's a piece of clustering algorithms

On Tue, Jun 21, 2011 at 11:34 PM, Marko Ciric <> wrote:

> Hi guys,
> When trying to do a content-based recommender, there could be two
> approaches
> with Apache Mahout:
>   - Having a custom implemented Taste ItemSimilarity that is calculated
>   with item features.
>   - Classifying a data set with Mahout by representing items with vectors.
> Has anybody had the experience with comparing performance/accuracy of
> those?
> Thanks
> --
> Marko Ćirić

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