## Uses of Interface
org.apache.mahout.cf.taste.impl.recommender.svd.Factorizer

Packages that use Factorizer
org.apache.mahout.cf.taste.impl.recommender.svd

Uses of Factorizer in org.apache.mahout.cf.taste.impl.recommender.svd

Classes in org.apache.mahout.cf.taste.impl.recommender.svd that implement Factorizer
`class` `AbstractFactorizer` base class for `Factorizer`s, provides ID to index mapping
`class` `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
`class` `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"
`class` `RatingSGDFactorizer` Matrix factorization with user and item biases for rating prediction, trained with plain vanilla SGD
`class` `SVDPlusPlusFactorizer` SVD++, an enhancement of classical matrix factorization for rating prediction.

Constructors in org.apache.mahout.cf.taste.impl.recommender.svd with parameters of type Factorizer
`SVDRecommender(DataModel dataModel, Factorizer factorizer)`
`SVDRecommender(DataModel dataModel, Factorizer factorizer, CandidateItemsStrategy candidateItemsStrategy)`
`SVDRecommender(DataModel dataModel, Factorizer factorizer, CandidateItemsStrategy candidateItemsStrategy, PersistenceStrategy persistenceStrategy)` Create an SVDRecommender using a persistent store to cache factorizations.
`SVDRecommender(DataModel dataModel, Factorizer factorizer, PersistenceStrategy persistenceStrategy)` Create an SVDRecommender using a persistent store to cache factorizations.

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