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From charlysf <charles.rue...@gmail.com>
Subject Re: Would like some recommendation, need advice
Date Mon, 22 Jun 2009 20:50:34 GMT

Thank you very much, in this case, I will give the best relevant articles,
but not really some new recommendations, in this case, I maybe should make
some recommendations about new subjects for an user, with an User Based
recommendation, and then make the same to retrieve linked articles.

That's it ?

That's my first part, implicit recommendation, and for explicit
recommendation, i have a user_feedback table, with : user_id, article_id,
rating, so for that this is very common and not a problem.

I'm wondering if I should choose a user based or an item basis engine for
that, what is your advice ?

Thanks! 


Ted Dunning wrote:
> 
> 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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