## Package org.apache.mahout.cf.taste.impl.recommender

Interface Summary
TopItems.Estimator<T>

Class Summary
AbstractCandidateItemsStrategy: Abstract base implementation for retrieving candidate items to recommend
AbstractRecommender
AllSimilarItemsCandidateItemsStrategy: returns the result of `ItemSimilarity.allSimilarItemIDs(long)` as candidate items
AllUnknownItemsCandidateItemsStrategy
ByValueRecommendedItemComparator: Defines a natural ordering from most-preferred item (highest value) to least-preferred.
CachingRecommender: A `Recommender` which caches the results from another `Recommender` in memory.
EstimatedPreferenceCapper: Simple class which encapsulates restricting a preference value to a predefined range.
GenericBooleanPrefItemBasedRecommender: A variant on `GenericItemBasedRecommender` which is appropriate for use when no notion of preference value exists in the data.
GenericBooleanPrefUserBasedRecommender: A variant on `GenericUserBasedRecommender` which is appropriate for use when no notion of preference value exists in the data.
GenericItemBasedRecommender: A simple `Recommender` which uses a given `DataModel` and `ItemSimilarity` to produce recommendations.
GenericItemBasedRecommender.MostSimilarEstimator
GenericRecommendedItem: A simple implementation of `RecommendedItem`.
GenericUserBasedRecommender: A simple `Recommender` which uses a given `DataModel` and `UserNeighborhood` to produce recommendations.
ItemAverageRecommender: A simple recommender that always estimates preference for an item to be the average of all known preference values for that item.
ItemUserAverageRecommender: Like `ItemAverageRecommender`, except that estimated preferences are adjusted for the users' average preference value.
NullRescorer<T>: A simple `Rescorer` which always returns the original score.
PreferredItemsNeighborhoodCandidateItemsStrategy
RandomRecommender: Produces random recommendations and preference estimates.
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.
SimilarUser: Simply encapsulates a user and a similarity value.
TopItems: A simple class that refactors the "find top N things" logic that is used in several places.

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