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| 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 "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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