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" <[email protected]> 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 <[email protected]>: >> 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. >> >> >> >>
