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From Lance Norskog <>
Subject Re: Singular vectors of a recommendation Item-Item space
Date Tue, 12 Jul 2011 03:56:23 GMT
SVDRecommender is intriguing, thanks for the pointer.

On Sun, Jul 10, 2011 at 12:15 PM, Ted Dunning <> wrote:
> Also, item-item similarity is often (nearly) the result of a matrix product.
>  If yours is, then you can decompose the user x item matrix and the desired
> eigenvalues are the singular values squared and the eigen vectors are the
> right singular vectors for the decomposition.
> On Sun, Jul 10, 2011 at 2:51 AM, Sean Owen <> wrote:
>> So it sounds like you want the SVD of the item-item similarity matrix?
>> Sure,
>> you can use Mahout for that. If you are not in Hadoop land then look at
>> SVDRecomnender to crib some related code. It is decomposing the user item
>> matrix though.
>> But for this special case of a symmetric matrix your singular vectors are
>> the eigenvectors which you may find much easier to compute.
>> I might restate the interpretation.
>> The 'size' of these vectors is not what matters to your question. It is
>> which elements (items) have the smallest vs largest values .
>> On Jul 10, 2011 3:08 AM, "Lance Norskog" <> wrote:

Lance Norskog

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