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Posted to dev@mahout.apache.org by myn <my...@163.com> on 2011/11/03 05:52:09 UTC
distributed item-based recommender
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.
Re: Re: distributed item-based recommender
Posted by Sean Owen <sr...@gmail.com>.
Yes, but what answer do you expect here? in this universe of 3 items,
that is the most reasonable estimate.
I understand the point that the weighted average can be high based on
just a few similar items, that are not that similar. That is a
weakness of this approach, but in practice it does not happen this way
much.
2011/11/3 myn <my...@163.com>:
> 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" <sr...@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 <my...@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.
>>>
>>>
>>>
>>>
>
Re:Re: distributed item-based recommender
Posted by myn <my...@163.com>.
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" <sr...@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 <my...@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.
>>
>>
>>
>>
Re: distributed item-based recommender
Posted by Sean Owen <sr...@gmail.com>.
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 <my...@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.
>
>
>
>