SVDRecommender is intriguing, thanks for the pointer.
On Sun, Jul 10, 2011 at 12:15 PM, Ted Dunning <ted.dunning@gmail.com> wrote:
> Also, itemitem 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 <srowen@gmail.com> wrote:
>
>> So it sounds like you want the SVD of the itemitem 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" <goksron@gmail.com> wrote:
>>
>

Lance Norskog
goksron@gmail.com
