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From Sean Owen <>
Subject Re: problems with GenericRecommenderIRStatsEvaluator:
Date Thu, 05 Nov 2009 15:31:34 GMT
On Thu, Nov 5, 2009 at 2:16 PM, michal shmueli <> wrote:
>         UserSimilarity userSimilarity = new
> TanimotoCoefficientSimilarity(dataModel);
>            UserNeighborhood neighborhood =  new
> NearestNUserNeighborhood(10, 0.0, userSimilarity, dataModel, 1.0);
>  recommender = new CachingRecommender(new
> GenericBooleanPrefUserBasedRecommender(dataModel, neighborhood,
> userSimilarity));
>      Also in here I can't see where i can define the "trainning" set size.

I don't either. A training set size is not a property of a
Recommender, it is a property of a RecommenderEvaluator.

But again, you are not using a RecommenderEvaluator! there is no
training set size in what you are doing.

>   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.

Yes, but, that is not the question here. We're talking about how to
choose *among the items the user already prefers* which ones are
relevant. There is no ranking there. You are talking about a ranking
among recommended items, which the user doesn't know about.

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