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From Ted Dunning <ted.dunn...@gmail.com>
Subject Re: Would like some recommendation, need advice
Date Mon, 22 Jun 2009 20:38:42 GMT
Indeed not.  But it *is* a case of the product architecture for
recommendations I was nattering about.

The problem here is how to compute the (user x topic)  x  (item x topic)'
product efficiently.  This can be done pretty well with either hadoop or
SQL.  In Pig or native map-reduce, the trick is to group by the topic and
then group by (user, item), summing the results as you go.  If either user x
topic or item x topic is small then a map-side join is good for the first
group-by operation.  If not, then doing two full-scale map-reduce operations
is no big deal.

You should consider how to weight different relevances, probably according
to overall frequency in the corpus.

On Mon, Jun 22, 2009 at 1:32 PM, Sean Owen <srowen@gmail.com> wrote:

> I see. This almost is not a 'classic' recommendation problem. If you
> have user-subject similarity, and subject-item similarity already,
> then user-item similarity is probably just the product of the two? so
> you can recommend items by ordering by similarity.
>

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