In general boolean recommender will get higher precision than using a
recommender with preferences, since the boolean considers every item
as good which is not true! So is there a way to make a realistic
measure from boolean ? For example, does dividing the precison by 2
makes sense since we get high precison using boolean?
Thanks
On Wed, Jan 23, 2013 at 3:49 PM, Ted Dunning <ted.dunn...@gmail.com> wrote:
> LLR should not be used to indicate proximity, but rather simply as a value
> to compare to a threshold.
>
> On Thu, Jan 24, 2013 at 1:45 AM, Zia mel <ziad.kame...@gmail.com> wrote:
>
>> OK . The TanimotoCoefficientSimilarity and LogLikelihoodSimilarity
>> used in MIA page 54 and 55 provide a score, so it seems they were not
>> using a Boolean recommender , something like code 1 maybe? Thanks
>>
>> On Tue, Jan 22, 2013 at 10:42 AM, Sean Owen <sro...@gmail.com> wrote:
>> > Yes any metric that concerns estimated value vs real value can't be
>> > used since all values are 1. Yes, when you use the non-boolean version
>> > with boolean data you always get 1. When you use the boolean version
>> > with boolean data you will get nonsense since the output of this
>> > recommender is not an estimated rating at all.
>> >
>> > On Tue, Jan 22, 2013 at 4:40 PM, Zia mel <ziad.kame...@gmail.com> wrote:
>> >> I got 0 when I used GenericUserBasedRecommender in code 2 but when
>> >> using GenericBooleanPrefUserBasedRecommender score was not 0 . I
>> >> repeat the test with different data and again I got some results.
>> >> Moreover , when I use
>> >> DataModel model = new FileDataModel(new File("ua.base"));
>> >> in code 2, the MAE score was higher.
>> >>
>> >> When you say RMSE can't be used with boolean data, I assume MAE also
>> >> can't be used?
>> >>
>> >> Thanks !
>> >>
>> >> On Tue, Jan 22, 2013 at 10:08 AM, Sean Owen <sro...@gmail.com> wrote:
>> >>> RMSE can't
>> >>> be used with boolean data.
>>
