Hi Steve,
If I understand you correctly your question is why it takes longer to
compute recommendations for all users than to run an evaluation with 0.7
as training percentage?
That would be because if you use 70% of the ratings for training, you
only need to estimate the preference for the remaining 30% of ratings.
--sebastian
On 18.10.2010 01:17, Steven Bourke wrote:
Hi,
I've previously tested a variety of recommenders from mahout using the
evaluation framework that comes built in (MAE, Precision and Recall).
I'm just now generating a full list of recommendations for users from my
dataset. Previously using this dataset has taken a matter of minutes to get
precision and recall results back using .7 as the training percentage. I now
notice that when I generate recommendations for all users in my dataset it
takes substantially longer to generate the entire list. Any idea what I
could be doing wrong?
My code is as follows
LongPrimitiveIterator userlist= model.getUserIDs();
while(userlist.hasNext())
{
Long id = userlist.next();
List<RecommendedItem> recommendations = recommender.recommend(id,
5);
for(RecommendedItem reco : recommendations)
{
System.out.println(id+" likes " + reco);
}
}
}
