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From Sean Owen <sro...@gmail.com>
Subject Re: distributed item-based recommender
Date Thu, 03 Nov 2011 06:37:59 GMT
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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