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!
