There should be something to solve this :) . For example, 2 users
having the same items could rate them 100% different , but using the
boolean their items will be recommended to each other.
Is there a chance that using preferences would get higher precison
that boolean? if so, when is that case?
On Thu, Jan 24, 2013 at 12:46 PM, Sean Owen <sro...@gmail.com> wrote:
> Not quite, the evaluation considers every item in the test set to be
> "good", but you would and should fix the test set size across
> evaluations for this reason. You are right that there is a big
> assumption there -- that everything in the test set is good. You have
> to believe your test split process supports that assumption.
>
> On Thu, Jan 24, 2013 at 6:37 PM, Zia mel <ziad.kame...@gmail.com> wrote:
>> 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.
>>>>
