# 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]], the term "SVD" often refers to [[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]]. | ||

− | * | + | == See also == |

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

− | == | + | == External links == |

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

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

+ | * [http://thatsmaths.com/2013/02/14/singularly-valuable-svd/ Singularly Valuable SVD] | ||

+ | |||

+ | [[Category: Method]] |

## Latest revision as of 06:47, 15 February 2013

**SVD** refers to **singular value decomposition** in linear algebra. However, in the field of collaborative filtering, the term "SVD" often refers to 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.

## See also

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