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

Packages that use Factorizer

Package	Description
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

Modifier and Type	Class and Description
class	AbstractFactorizer base class for Factorizers, 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

Constructor and Description
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.