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

Interface Summary
Factorizer Implementation must be able to create a factorization of a rating matrix
PersistenceStrategy Provides storage for Factorizations
 

Class Summary
AbstractFactorizer base class for Factorizers, provides ID to index mapping
ALSWRFactorizer factorizes the rating matrix using "Alternating-Least-Squares with Weighted-λ-Regularization" as described in the paper "Large-scale Collaborative Filtering for the Netflix Prize"
ExpectationMaximizationSVDFactorizer Calculates the SVD using an Expectation Maximization algorithm.
Factorization a factorization of the rating matrix
FilePersistenceStrategy Provides a file-based persistent store.
NoPersistenceStrategy A PersistenceStrategy which does nothing.
SVDRecommender A Recommender that uses matrix factorization (a projection of users and items onto a feature space)
 



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