## Uses of Interface
org.apache.mahout.cf.taste.recommender.CandidateItemsStrategy

Packages that use CandidateItemsStrategy
org.apache.mahout.cf.taste.impl.recommender
org.apache.mahout.cf.taste.impl.recommender.svd

Uses of CandidateItemsStrategy in org.apache.mahout.cf.taste.impl.recommender

Classes in org.apache.mahout.cf.taste.impl.recommender that implement CandidateItemsStrategy
`class` `AbstractCandidateItemsStrategy` Abstract base implementation for retrieving candidate items to recommend
`class` `AllSimilarItemsCandidateItemsStrategy` returns the result of `ItemSimilarity.allSimilarItemIDs(long)` as candidate items
`class` `AllUnknownItemsCandidateItemsStrategy`
`class` `PreferredItemsNeighborhoodCandidateItemsStrategy`
`class` `SamplingCandidateItemsStrategy` Returns all items that have not been rated by the user (3) and that were preferred by another user (2) that has preferred at least one item (1) that the current user has preferred too.

Methods in org.apache.mahout.cf.taste.impl.recommender that return CandidateItemsStrategy
`protected static CandidateItemsStrategy` `AbstractRecommender.getDefaultCandidateItemsStrategy()`

Constructors in org.apache.mahout.cf.taste.impl.recommender with parameters of type CandidateItemsStrategy
`AbstractRecommender(DataModel dataModel, CandidateItemsStrategy candidateItemsStrategy)`
`GenericBooleanPrefItemBasedRecommender(DataModel dataModel, ItemSimilarity similarity, CandidateItemsStrategy candidateItemsStrategy, MostSimilarItemsCandidateItemsStrategy mostSimilarItemsCandidateItemsStrategy)`
`GenericItemBasedRecommender(DataModel dataModel, ItemSimilarity similarity, CandidateItemsStrategy candidateItemsStrategy, MostSimilarItemsCandidateItemsStrategy mostSimilarItemsCandidateItemsStrategy)`

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

Constructors in org.apache.mahout.cf.taste.impl.recommender.svd with parameters of type CandidateItemsStrategy
`SVDRecommender(DataModel dataModel, Factorizer factorizer, CandidateItemsStrategy candidateItemsStrategy)`
`SVDRecommender(DataModel dataModel, Factorizer factorizer, CandidateItemsStrategy candidateItemsStrategy, PersistenceStrategy persistenceStrategy)` Create an SVDRecommender using a persistent store to cache factorizations.

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