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

Interface Summary
ClusterSimilarity: Returns the "similarity" between two clusters of users, according to some definition of similarity.
TopItems.Estimator<T>

Class Summary
AbstractRecommender
AllUnknownItemsCandidateItemsStrategy: returns all items the user has not rated yet
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.
FarthestNeighborClusterSimilarity: Defines cluster similarity as the smallest similarity between any two users in the clusters -- that is, it says that clusters are close when all pairs of their members have relatively high similarity.
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.
NearestNeighborClusterSimilarity: Defines cluster similarity as the largest similarity between any two users in the clusters -- that is, it says that clusters are close when some pair of their members has high similarity.
NullRescorer<T>: A simple `Rescorer` which always returns the original score.
PreferredItemsNeighborhoodCandidateItemsStrategy: 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
RandomRecommender: Produces random recommendations and preference estimates.
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
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.
TreeClusteringRecommender: A `Recommender` that clusters users, then determines the clusters' top recommendations.
TreeClusteringRecommender2: A `Recommender` that clusters users, then determines the clusters' top recommendations.

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