It has been a while since we talked about this topic, but the score returned is
not just adding up the similarity values that anybody in the neighborhood has
to the item. IT is atucally devided by the totalSimilarity. As result, I think
the score is still 1.0. Did I miss something? See the codes below.
for(User user : theNeighborhood) {if(!user.equals(theUser)) {// See
GenericItemBasedRecommender.doEstimatePreference() tooPreference pref =
user.getPreferenceFor(itemID);
preference += theSimilarity * pref.getValue();
totalSimilarity += theSimilarity;
}
}
}
}
________________________________
From: Sean Owen <[email protected]>
To: [email protected]
Sent: Tue, July 28, 2009 10:18:11 AM
Subject: Re: recommender on binary data
Yeah I just mean that it should in theory always return 1.0, but it
doesn't, in order to return something useful. Take a look at
estimatePreference(). It is just adding up all the similarity values
that anybody in the neighborhood has to the item. This simply favors
items that more users like you are associated to, which is a
reasonable heuristic for ordering the recommendations.
On Tue, Jul 28, 2009 at 4:15 PM, James James<[email protected]> wrote:
> Thanks, can you elaborate a little bit on "I abuse the value of the estimated
> preference in this case to return differing values depending on the
> strength of the association between the user and item". Is this reflected in
> the codes somewhere?
>
> Thanks,
>
>
>
>
> ________________________________
> From: Sean Owen <[email protected]>
> To: [email protected]
> Sent: Tuesday, July 28, 2009 10:11:55 AM
> Subject: Re: recommender on binary data
>
> Well.. good question. Really, I abuse the value of the estimated
> preference in this case to return differing values depending on the
> strength of the association between the user and item. It's not always
> 1.0 actually.
>
> It is certainly 'wrong' in a theoretical sense but seemed more useful
> than just always saying '1.0'.
>
> On Tue, Jul 28, 2009 at 4:09 PM, James James<[email protected]>
> wrote:
>> Thanks, I think the method recommend() returns an ordered list of items from
>> the test items. If the return value of estimatePreference() is all the same
>> for all the test items, how does the method recommend() decide the order in
>> which it puts each test item on the ordered list.
>
>
>
>
if(pref != null) {doubletheSimilarity = similarity.userSimilarity(theUser,
user) + 1.0;if(!Double.isNaN(theSimilarity)) {returntotalSimilarity == 0.0 ?
Double.NaN: preference / totalSimilarity;
