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From Najum Ali <naju...@googlemail.com>
Subject Confusion using Mahout Recommender Evaluation
Date Thu, 08 May 2014 10:52:19 GMT


Anfang der weitergeleiteten Nachricht:

> Von: Najum Ali <najum89@googlemail.com>
> Betreff: Confusion using Mahout Recommender Evaluation
> Datum: 8. Mai 2014 12:44:01 MESZ
> An: user@mahout.apache.org
> 
> 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!


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