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From James James <>
Subject Re: problems with GenericRecommenderIRStatsEvaluator:
Date Thu, 05 Nov 2009 14:45:59 GMT
 Actually, there is, in the sense that you can have items that are more
likely to be purchase (if we assume that the data is {userid, itemId,1} and
1 represents that the user bought itemId). So you can ranked the item that
you recommend. Thus, if I ask for k=3, there is some ranked order of the
item that you would recommend.
Again, maybe it's being supported already, beacuse when asking
recommendation from the servlet, i get ranked list (even under the setting
of the boolean rec.)

-->  My understanding is that the returned recommendentation list is a ranked list of
items based on their predicted scores.
Even under the setting of the boolean, each candidate item to be recommended gets a different
predicted score. So I think that you are right they are also ranked.


From: michal shmueli <>
Sent: Thu, November 5, 2009 8:16:54 AM
Subject: Re: problems with GenericRecommenderIRStatsEvaluator:

On Thu, Nov 5, 2009 at 3:33 PM, Sean Owen <> wrote:

> On Thu, Nov 5, 2009 at 1:28 PM, michal shmueli <>
> wrote:
> >>    - which recommenderEvaluator would you suggest for Boolean data? does
> it
> > also mean that I need to change the recommender that i'm using (which use
> > the Tanimoto similarity) ?
> None of them will work with RecommenderEvaluator. It is trying to
> evaluate how well estimated and actual preferences match, but, with
> boolean data, these are all always 1, so it's meaningless. You are
> doing the right thing.

>  - OK, so maybe what i'm missing here is the recommender itself. I went
> back to the IR evaluator, and I see that it compare the preferences of the
> user (say, items that appears in her list), with the items it gets from
> similar users.
  So I'm going one step back, to the similarity, and the recommender:

        UserSimilarity userSimilarity = new
            UserNeighborhood neighborhood =  new
NearestNUserNeighborhood(10, 0.0, userSimilarity, dataModel, 1.0);
recommender = new CachingRecommender(new
GenericBooleanPrefUserBasedRecommender(dataModel, neighborhood,

      Also in here I can't see where i can define the "trainning" set size.

> >  - It seems that you give the same results if you report on the best-2
> > (ranked 1st and 2nd) out of k=10 or the worst-2 (9th and 10th) from the
> 10,
> > but again , I might be wrong.
> What do you mean by 1st, 2nd, 9th, 10th? in your data set there is no
> notion of an item being "most liked" by a user.

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