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From Sebastian Schelter <...@apache.org>
Subject Re: SVDRecommender
Date Tue, 21 Sep 2010 15:06:41 GMT
Hi James,

I'm not sure either, but regarding the comment about Simon Funk, I'd
guess that the implementation is based on Simon Funk's famous Netflix
post, at least the code looks very similar to his approach. Correct me
if I'm wrong.

Simon Funk, "Netflix Update: Try this at home"
http://sifter.org/~simon/journal/20061211.html
<http://sifter.org/%7Esimon/journal/20061211.html>

-- sebastian

Am 21.09.2010 17:01, schrieb Sean Owen:
> On Tue, Sep 21, 2010 at 3:53 PM, James James
> <recommendersystem@yahoo.com> wrote:
>   
>> Hi,
>>
>> I was looking at the implementation of the SVDRecommender, and was wondering if
>> anyone could point me to a paper or an algorithm on which the implementation is
>> based.
>>     
> This was asked recently on the mailing list, and all I knew was:
> I'm not the author, but understand that the implementation is based on
> an expectation maximization approach. I don't know the best resource,
> nor the one that might have informed the implementation, but I suspect
> this is basically it:
>
> http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.68.1154&rep=rep1&type=pdf
>
>   
>> In addition, I was wondering if the current implementation supports
>> binary-rating data (all we know is whether a piece of content is consumed or
>> not).
>>     
> You specifically mean the SVD implementation?
> Sure you can feed in 1 and 0 and see the result (which will not be 1
> and 0). It's not the most usual application for an SVD and I've never
> done it.
>
>
>   
>> The current implementation relies on a value which is the average score given by
>> all the users to all the items. In case of binary-rating data, there are no
>> explicit scores given. However, if we assign 1 to all consumed items and 0 to
>> all unconsumed items, we will be able to get an average score needed. Has
>> anybody tried the SVDRecommender on such binary-rating data before?
>>     


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