Confusion using AverageAbsoluteDifferenceRecommenderEvaluator #evaluate method!

[Najum Ali <na...@googlemail.com>]

Hi,

I have a question about using the AverageAbsoluteDifferenceRecommenderEvaluator #evaluate method. 

Using a GenericUserBasedRecommender:

new RecommenderBuilder() {
@Override
public Recommender buildRecommender(DataModel model) throws TasteException {
UserSimilarity userSimilarity = new PearsonCorrelationSimilarity(model);
UserNeighborhood userNeighborhood = new NearestNUserNeighborhood(50, userSimilarity, model);
return new GenericUserBasedRecommender(model, userNeighborhood, userSimilarity);
	}
};

the AverageAbsoluteDifferenceRecommenderEvaluator prints this in the beginning time:

12:02:04.000 [pool-1-thread-1] INFO  org.apache.mahout.cf.taste.impl.eval.StatsCallable - Average time per recommendation: 127ms
12:02:04.000 [pool-1-thread-1] INFO  org.apache.mahout.cf.taste.impl.eval.StatsCallable - Approximate memory used: 826MB / 960MB

And getting a recommendation with recommenderBuilder.buildRecommender(model).recommend(101, 10); - takes about
186.091 ms .. thats pretty much something like the average time per recommendation from the AverageAbsoluteDifferenceRecommenderEvaluator


Now, with GenericItemBasedRecommender following happens:

new RecommenderBuilder() {
@Override
public Recommender buildRecommender(DataModel model) throws TasteException {
ItemSimilarity itemSimilarity = new PearsonCorrelationSimilarity(model);
return new GenericItemBasedRecommender(model, itemSimilarity);
	}
};

Evaluation Output:
11:59:19.950 [main] INFO  o.a.m.c.t.i.eval.AbstractDifferenceRecommenderEvaluator - Starting timing of 63493 tasks in 8 threads
11:59:19.979 [pool-1-thread-1] INFO  org.apache.mahout.cf.taste.impl.eval.StatsCallable - Average time per recommendation: 26ms
11:59:19.979 [pool-1-thread-1] INFO  org.apache.mahout.cf.taste.impl.eval.StatsCallable - Approximate memory used: 598MB / 897MB

yea .. it´s every time something like 26ms

But in fact, using this Recommender I have to wait a looong time for an answer - recommenderBuilder.buildRecommender(model).recommend(101, 10);

GenericItemBasedRecommender —> 49267.09 ms  .. or sometimes less ...but never and ever under 100ms !!

I am using the GroupLens 10M data with GroupLensDataModel and the evaluation I am using 0.95 trainingPercentage data and 1.0 evaluationPercentage

With the full evaluationPercentage i will make sure that the recommendations created, takes all items into account.. so I can compare the average recommendation time with a normal recommendation

Why is the average time per recommendation so less by using a GenericItemBasedRecommender with AverageAbsoluteDifferenceRecommenderEvaluator?? But in face it is not that fast using the method #recommend

I don´t get it .. hope someone clear this out !! Thanks!