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From Sean Owen <sro...@gmail.com>
Subject Re: Recommender with item features
Date Sat, 12 May 2012 09:56:17 GMT
You can write your own ItemSimilarity metric based on the features and
then use an item-based recommender. That piece you'd have to do
yourself by making up some notion of similarity; if the features were
all numeric and normalized you can look at repurposing something based
on Euclidean distance or cosine similarity or something, but I don't
know if those are the features you have.

Sean

On Sat, May 12, 2012 at 2:15 AM, EDUARDO ANTONIO BUITRAGO ZAPATA
<eduardobuitrago@gmail.com> wrote:
> Hi All,
>
> As far I've seen, all mahout recommenders uses this setting for input file:
>
>                User1, item1, rating1
>                User1, item2, rating2
>               ...
>                User2, item1, rating3
>
> What I need to do is a recommender for new digital cameras. I want to know
> which user is more interested in a camera when it arrives, then I can make
> recommendation. To do that,  I want to take into account the camera
> features (optical zoom, LCD size, etc).  ¿Is there a way to implement this
> in mahout? Maybe the input file is something like this:
>
>                User1, item1, feat1, feat2, … , featn, rating1
>                User1, item2, feat1, feat2, … , featN, rating2
>                ...
>                User1, itemN, feat1, feat2, … , featN, rating3
>                User1, newItem, feat1, feat2, … , featn, ?
>
> Any help would be appreciated.
>
> --
> Eduardo** <ea.buitrago73@uniandes.edu.co>

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