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Packages that use Preference | |
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org.apache.mahout.cf.taste.impl.eval | |
org.apache.mahout.cf.taste.impl.model | |
org.apache.mahout.cf.taste.impl.recommender.svd | |
org.apache.mahout.cf.taste.model |
Uses of Preference in org.apache.mahout.cf.taste.impl.eval |
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Methods in org.apache.mahout.cf.taste.impl.eval with parameters of type Preference | |
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protected void |
RMSRecommenderEvaluator.processOneEstimate(float estimatedPreference,
Preference realPref)
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protected void |
AverageAbsoluteDifferenceRecommenderEvaluator.processOneEstimate(float estimatedPreference,
Preference realPref)
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protected abstract void |
AbstractDifferenceRecommenderEvaluator.processOneEstimate(float estimatedPreference,
Preference realPref)
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Uses of Preference in org.apache.mahout.cf.taste.impl.model |
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Classes in org.apache.mahout.cf.taste.impl.model that implement Preference | |
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class |
BooleanPreference
Encapsulates a simple boolean "preference" for an item whose value does not matter (is fixed at 1.0). |
class |
GenericPreference
A simple Preference encapsulating an item and preference value. |
Methods in org.apache.mahout.cf.taste.impl.model that return Preference | |
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Preference |
GenericUserPreferenceArray.get(int i)
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Preference |
GenericItemPreferenceArray.get(int i)
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Preference |
BooleanUserPreferenceArray.get(int i)
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Preference |
BooleanItemPreferenceArray.get(int i)
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Methods in org.apache.mahout.cf.taste.impl.model that return types with arguments of type Preference | |
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Iterator<Preference> |
GenericUserPreferenceArray.iterator()
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Iterator<Preference> |
GenericItemPreferenceArray.iterator()
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Iterator<Preference> |
BooleanUserPreferenceArray.iterator()
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Iterator<Preference> |
BooleanItemPreferenceArray.iterator()
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Methods in org.apache.mahout.cf.taste.impl.model with parameters of type Preference | |
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void |
GenericUserPreferenceArray.set(int i,
Preference pref)
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void |
GenericItemPreferenceArray.set(int i,
Preference pref)
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void |
BooleanUserPreferenceArray.set(int i,
Preference pref)
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void |
BooleanItemPreferenceArray.set(int i,
Preference pref)
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Method parameters in org.apache.mahout.cf.taste.impl.model with type arguments of type Preference | |
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static FastByIDMap<PreferenceArray> |
GenericDataModel.toDataMap(FastByIDMap<Collection<Preference>> data,
boolean byUser)
Swaps, in-place, List s for arrays in Map values . |
Constructor parameters in org.apache.mahout.cf.taste.impl.model with type arguments of type Preference | |
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BooleanItemPreferenceArray(List<? extends Preference> prefs,
boolean forOneUser)
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BooleanUserPreferenceArray(List<? extends Preference> prefs)
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GenericItemPreferenceArray(List<? extends Preference> prefs)
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GenericUserPreferenceArray(List<? extends Preference> prefs)
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Uses of Preference in org.apache.mahout.cf.taste.impl.recommender.svd |
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Methods in org.apache.mahout.cf.taste.impl.recommender.svd that return Preference | |
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Preference |
ParallelSGDFactorizer.PreferenceShuffler.get(int i)
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Methods in org.apache.mahout.cf.taste.impl.recommender.svd with parameters of type Preference | |
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protected void |
ParallelSGDFactorizer.update(Preference preference,
double mu)
TODO: this is the vanilla sgd by Tacaks 2009, I speculate that using scaling technique proposed in: Towards Optimal One Pass Large Scale Learning with Averaged Stochastic Gradient Descent section 5, page 6 can be beneficial in term s of both speed and accuracy. |
Uses of Preference in org.apache.mahout.cf.taste.model |
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Methods in org.apache.mahout.cf.taste.model that return Preference | |
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Preference |
PreferenceArray.get(int i)
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Methods in org.apache.mahout.cf.taste.model with parameters of type Preference | |
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void |
PreferenceArray.set(int i,
Preference pref)
Sets preference at i from information in the given Preference |
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