## Uses of Interface
org.apache.mahout.cf.taste.model.Preference

Packages that use Preference
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

Methods in org.apache.mahout.cf.taste.impl.eval with parameters of type Preference
`protected void` `RMSRecommenderEvaluator.processOneEstimate(float estimatedPreference, Preference realPref)`
`protected void` `AverageAbsoluteDifferenceRecommenderEvaluator.processOneEstimate(float estimatedPreference, Preference realPref)`
`protected abstract void` `AbstractDifferenceRecommenderEvaluator.processOneEstimate(float estimatedPreference, Preference realPref)`

Uses of Preference in org.apache.mahout.cf.taste.impl.model

Classes in org.apache.mahout.cf.taste.impl.model that implement Preference
`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
`Preference` `GenericUserPreferenceArray.get(int i)`
`Preference` `GenericItemPreferenceArray.get(int i)`
`Preference` `BooleanUserPreferenceArray.get(int i)`
`Preference` `BooleanItemPreferenceArray.get(int i)`

Methods in org.apache.mahout.cf.taste.impl.model that return types with arguments of type Preference
`Iterator<Preference>` `GenericUserPreferenceArray.iterator()`
`Iterator<Preference>` `GenericItemPreferenceArray.iterator()`
`Iterator<Preference>` `BooleanUserPreferenceArray.iterator()`
`Iterator<Preference>` `BooleanItemPreferenceArray.iterator()`

Methods in org.apache.mahout.cf.taste.impl.model with parameters of type Preference
`void` `GenericUserPreferenceArray.set(int i, Preference pref)`
`void` `GenericItemPreferenceArray.set(int i, Preference pref)`
`void` `BooleanUserPreferenceArray.set(int i, Preference pref)`
`void` `BooleanItemPreferenceArray.set(int i, Preference pref)`

Method parameters in org.apache.mahout.cf.taste.impl.model with type arguments of type Preference
`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
`BooleanItemPreferenceArray(List<? extends Preference> prefs, boolean forOneUser)`
`BooleanUserPreferenceArray(List<? extends Preference> prefs)`
`GenericItemPreferenceArray(List<? extends Preference> prefs)`
`GenericUserPreferenceArray(List<? extends Preference> prefs)`

Uses of Preference in org.apache.mahout.cf.taste.impl.recommender.svd

Methods in org.apache.mahout.cf.taste.impl.recommender.svd that return Preference
`Preference` `ParallelSGDFactorizer.PreferenceShuffler.get(int i)`

Methods in org.apache.mahout.cf.taste.impl.recommender.svd with parameters of type Preference
`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

Methods in org.apache.mahout.cf.taste.model that return Preference
`Preference` `PreferenceArray.get(int i)`

Methods in org.apache.mahout.cf.taste.model with parameters of type Preference
`void` `PreferenceArray.set(int i, Preference pref)` Sets preference at i from information in the given `Preference`

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