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java.lang.Objectorg.apache.mahout.cf.taste.impl.recommender.AbstractCandidateItemsStrategy
org.apache.mahout.cf.taste.impl.recommender.SamplingCandidateItemsStrategy
public class SamplingCandidateItemsStrategy
returns all items that have not been rated by the user and that were preferred by another user that has preferred at least one item that the current user has preferred too
this strategy uses sampling in a way that only a certain amount of preferences per item is considered
max(defaultMaxPrefsPerItemConsidered, userItemCountFactor * log(max(N_users, N_items)))
Constructor Summary | |
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SamplingCandidateItemsStrategy()
uses defaultMaxPrefsPerItemConsidered = 100 and userItemCountMultiplier = 20 as default values |
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SamplingCandidateItemsStrategy(int defaultMaxPrefsPerItemConsidered,
int userItemCountMultiplier)
the maximum number of prefs considered per item will be computed like this: |
Method Summary | |
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protected FastIDSet |
doGetCandidateItems(long[] preferredItemIDs,
DataModel dataModel)
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Methods inherited from class org.apache.mahout.cf.taste.impl.recommender.AbstractCandidateItemsStrategy |
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getCandidateItems, getCandidateItems |
Methods inherited from class java.lang.Object |
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clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Constructor Detail |
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public SamplingCandidateItemsStrategy()
SamplingCandidateItemsStrategy(int, int)
public SamplingCandidateItemsStrategy(int defaultMaxPrefsPerItemConsidered, int userItemCountMultiplier)
the maximum number of prefs considered per item will be computed like this:
max(defaultMaxPrefsPerItemConsidered, userItemCountFactor * log(max(N_users, N_items)))
defaultMaxPrefsPerItemConsidered
- userItemCountMultiplier
- Method Detail |
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protected FastIDSet doGetCandidateItems(long[] preferredItemIDs, DataModel dataModel) throws TasteException
TasteException
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