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From michal shmueli <>
Subject Re: problems with GenericRecommenderIRStatsEvaluator:
Date Thu, 05 Nov 2009 06:58:36 GMT
Thanks ! Sean.
The eval now terminates successfully.
If I may, i have few more clarifications questions:

1. At the end of the eval run I'm getting this information:
09/11/04 15:59:48 INFO grouplens.

Is this the aggregated statistics over all users?

2. Given that these are the params that i use for eval:
 IRStatistics stats =
      evaluator.evaluate(new GroupLensRecommenderBuilder(),
null,model, null, 5, 1.0,0.7);

Just to make it more clear, for the 0.7 (last param), does it mean that for
each user we use 70% of the data to learn and 30% for test?

3. Scalability of the Boolean recommneder ? I'm using this setting:

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

Is this recommender is scalable by the number of users?

4. Some issue that I still didn't understand... I know that the Taste demo
doesn't required Hadoop.
I wonder when the Hadoop is required? Thus, assume I implemented these 4

   - DataModel
   - UserSimilarity and ItemSimilarity
   - UserNeighborhood
   - Recommender

Now, assume that I use some existing classes (the Boolean), where exactly
(if at all) the Hadoop is required? I can't find good documentation that
explain this issue.


On Wed, Nov 4, 2009 at 2:48 PM, Sean Owen <> wrote:

> Looks like a small bug to me. When it happens that *all* the items
> from one user are considered relevant, and, that user happens to be
> the one being evaluated, this happens.
> Easy to fix and I'll commit it in just a second here. Get the latest
> from SVN in a few minutes and try agian.
> On Wed, Nov 4, 2009 at 12:22 PM, michal shmueli
> <> wrote:
> > Hi,
> >
> > I'm trying to evaluate the quality of the Boolean recommender. (I have no
> > ratings in my data but only: (userId,itemId,1) 1 for all entries.
> > I'm using this setting for the recommender:

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