## Package org.apache.mahout.cf.taste.hadoop.item

Class Summary
AggregateAndRecommendReducer: computes prediction values for each user
ItemFilterAsVectorAndPrefsReducer: we use a neat little trick to explicitly filter items for some users: we inject a NaN summand into the preference estimation for those items, which makes `AggregateAndRecommendReducer` automatically exclude them
ItemFilterMapper: map out all user/item pairs to filter, keyed by the itemID
ItemIDIndexMapper
ItemIDIndexReducer
PartialMultiplyMapper: maps similar items and their preference values per user
PrefAndSimilarityColumnWritable
RecommenderJob: Runs a completely distributed recommender job as a series of mapreduces.
SimilarityMatrixRowWrapperMapper: maps a row of the similarity matrix to a `VectorOrPrefWritable` actually a column from that matrix has to be used but as the similarity matrix is symmetric, we can use a row instead of having to transpose it
ToUserVectorsReducer: Input
ToVectorAndPrefReducer
UserVectorSplitterMapper
VectorAndPrefsWritable
VectorOrPrefWritable

Enum Summary
ToUserVectorsReducer.Counters

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