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 07:47, 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.
