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 05:49, 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.
