Anfang der weitergeleiteten Nachricht:
> Von: Najum Ali <naju...@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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