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Packages that use Factorizer | |
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org.apache.mahout.cf.taste.impl.recommender.svd |
Uses of Factorizer in org.apache.mahout.cf.taste.impl.recommender.svd |
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Classes in org.apache.mahout.cf.taste.impl.recommender.svd that implement Factorizer | |
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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 | |
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SVDRecommender(DataModel dataModel,
Factorizer factorizer)
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SVDRecommender(DataModel dataModel,
Factorizer factorizer,
CandidateItemsStrategy candidateItemsStrategy)
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SVDRecommender(DataModel dataModel,
Factorizer factorizer,
CandidateItemsStrategy candidateItemsStrategy,
PersistenceStrategy persistenceStrategy)
Create an SVDRecommender using a persistent store to cache factorizations. |
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SVDRecommender(DataModel dataModel,
Factorizer factorizer,
PersistenceStrategy persistenceStrategy)
Create an SVDRecommender using a persistent store to cache factorizations. |
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