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''' often means [[Matrix factorization]].
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'''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 '''SVD''' can also be used as collaborative filtering algorithm. It's also named Latent Semantic Indexing(LSI) in IR.
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The traditional SVD can also be used as collaborative filtering algorithm.
 
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It is also named '''latent semantic indexing''' ('''LSI''') in [[information retrieval]].
* Wikipedia page about SVD: http://en.wikipedia.org/wiki/Singular_value_decomposition
 
* Wikipedia page about LSI: http://en.wikipedia.org/wiki/Latent_semantic_indexing
 
  
 
== 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]]
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== External links ==
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* [[Wikipedia: Singular value decomposition]]
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* [[Wikipedia: Latent semantic indexing]]
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[[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.

External links