|
||||||||||
PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES |
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) |
|
||||||||||
PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES |