## 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 `Factorization`s

Class Summary
AbstractFactorizer: base class for `Factorizer`s, provides ID to index mapping
ALSWRFactorizer: factorizes the rating matrix using "Alternating-Least-Squares with Weighted-λ-Regularization" as described in "Large-scale Collaborative Filtering for the Netflix Prize" also supports the implicit feedback variant of this approach as described in "Collaborative Filtering for Implicit Feedback Datasets" available at http://research.yahoo.com/pub/2433
Factorization: a factorization of the rating matrix
FilePersistenceStrategy: Provides a file-based persistent store.
NoPersistenceStrategy: A `PersistenceStrategy` which does nothing.
ParallelSGDFactorizer: Minimalistic implementation of Parallel SGD factorizer based on "Scalable Collaborative Filtering Approaches for Large Recommender Systems" and "Hogwild!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent"
ParallelSGDFactorizer.PreferenceShuffler
RatingSGDFactorizer: Matrix factorization with user and item biases for rating prediction, trained with plain vanilla SGD
SVDPlusPlusFactorizer: SVD++, an enhancement of classical matrix factorization for rating prediction.
SVDRecommender: A `Recommender` that uses matrix factorization (a projection of users and items onto a feature space)

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