Why does evaluating a recommender take far less time than actually generating results?

[Steven Bourke]

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);

         }



         }

}