(Resending mail without sending my digital signature)
Hi there,
I mentioned a problem of using the ItemBasedRecommender. It is so much slower
then using UserBasedRecommender.
@Sebastian: You said limiting the precomputation file should work. For example:
only 50 similarities for an Item. You also said this feature is not included
in the precomputation yet.
Although using the MultithreadedBatchItemSimilarities (Mahout 0.9), I saw that
the Constructor accepts following arguments:
/**
* @param recommender recommender to use
* @param similarItemsPerItem number of similar items to compute per item
*/
public MultithreadedBatchItemSimilarities(ItemBasedRecommender recommender,
int similarItemsPerItem) {
this(recommender, similarItemsPerItem, DEFAULT_BATCH_SIZE);
}
And in fact, if I set 15 as similarItemsPerItem, the csv file contains only 15
similar items per item. Why do you said that this feature is not implemented
yet, maybe you meant something else and I
understood something wrong. Therefore I am confused at bit. The problem is,
also with limited pairs of similar items the user-based approach is much faster:
Using a file with 6040 users and 3706 items:
A UserbasedRecommender with k = 50 takes 331ms.
Itembased takes 1510 ms and with precomputed similarities it takes 836 .. still
double as slow. Is there no possibility to restrict something like
„neighborhood size“ in userbased?
I have also tried SamplingCandidateItemsStrategy with e.g 10 on each three
first arguments .. and also tried using CachingSimilarity decorater, but
nothing seems to help.
Find a attached java file for this test.
And yea, I am using the GroupLens Movie Data: 1M.
Can the dataset be the fault as Sebastian mentioned before:
>>>>>> In the movielens dataset this is true for almost all pairs of items,
>>>>>> unfortunately. From 3076 items, more than 11 million similarities are
>>>>>> created. A common approach for that (which is not yet implemented in
>>>>>> our
>>>>>> precomputation unfortunately) is to only retain the top-k similar items
Hope of getting some help of u guys.. this is getting very depressing :(
Regards
Najum Ali
