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From myn <...@163.com>
Subject Re:Re: distributed item-based recommender
Date Thu, 03 Nov 2011 07:32:00 GMT
for example
similarity(2,1)=1
similarity(2,2)=1
similarity(3,1)=0.000001
similarity(3,2)=0.000001

rating(u,1))=2
rating(u,2)) =2
 
Prediction(u,2)= (1*2+1*2)/(1+1)=2
Prediction(u,3)= (0.000001*2+0.000001*2)/(0.000001+0.000001)=2
but item2 and item3 is quite different 
 
i have search lots about cf,bug all is used that
Prediction(u,i) = sum(all n from N: similarity(i,n) * rating(u,n)) / sum(all n from N: abs(similarity(i,n)))



At 2011-11-03 14:37:59,"Sean Owen" <srowen@gmail.com> wrote:
>The formula here is just a weighted average. You have to divide by the
>sum of the weights to normalize the result.
>
>If similarity(i,n) is small, then the denominator is small, yes. But
>so is the numerator. This does not make the result large.
>
>2011/11/3 myn <myn@163.com>:
>> in the pagehttps://issues.apache.org/jira/browse/MAHOUT-420
>>
>> Prediction(u,i) = sum(all n from N: similarity(i,n) * rating(u,n)) / sum(all n from
N: abs(similarity(i,n)))
>>
>> why must devide sum(all n from N: abs(similarity(i,n))),  if similarity(i,n) is quite
small , i don`t want redommend that item,i only want recommend  very similary item. it seems
that not work very well.
>>
>>
>>
>>

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