# Difference between revisions of "Singular value decomposition"

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− | '''SVD''' refers to singular value decomposition in linear algebra. However, in the field of collaborative filtering, | + | '''SVD''' refers to '''singular value decomposition''' in linear algebra. However, in the field of collaborative filtering, SVD often means [[matrix factorization]]. |

== SVD in Math == | == SVD in Math == | ||

− | The traditional | + | The traditional SVD can also be used as collaborative filtering algorithm. |

− | + | It is also named '''latent semantic indexing''' ('''LSI''') in [[information retrieval]]. | |

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== SVD as Matrix Factorization == | == SVD as Matrix Factorization == | ||

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* [[SVD++]] is a generalization of matrix factorization to make use of implicit feedback. | * [[SVD++]] is a generalization of matrix factorization to make use of implicit feedback. | ||

− | [[Category:Method]] | + | == External links == |

+ | * [[Wikipedia: Singular value decomposition]] | ||

+ | * [[Wikipedia: Latent semantic indexing]] | ||

+ | |||

+ | [[Category: Method]] |

## Revision as of 04:49, 12 November 2011

**SVD** refers to **singular value decomposition** in linear algebra. However, in the field of collaborative filtering, SVD often means matrix factorization.

## SVD in Math

The traditional SVD can also be used as collaborative filtering algorithm.
It is also named **latent semantic indexing** (**LSI**) in information retrieval.

## SVD as Matrix Factorization

Please refer to Matrix factorization

## See Also

- SVD++ is a generalization of matrix factorization to make use of implicit feedback.