Difference between revisions of "Matrix factorization"
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The approximation of a [[matrix]] by several lower-rank matrices is called '''matrix factorization'''. Matrix factorization methods are popular [[collaborative filtering]] methods, often with good [[scalability]] properties and predictive [[accuracy]]. | The approximation of a [[matrix]] by several lower-rank matrices is called '''matrix factorization'''. Matrix factorization methods are popular [[collaborative filtering]] methods, often with good [[scalability]] properties and predictive [[accuracy]]. | ||
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| + | == Literature == | ||
| + | * [[Yehuda Koren]], [[Robert Bell]], [[Chris Volinsky]]: ''[http://research.yahoo.com/files/ieeecomputer.pdf Matrix Factorization Techniques for Recommender Systems.]'' IEEE Computer, 2009 | ||
| + | * Rainer Gemulla, Peter Haas, Erik Nijkamp, Yannis Sismanis: ''Large-Scale Matrix Factorization with Distributed Stochastic Gradient Descent'', [[KDD 2011]] | ||
== External links == | == External links == | ||
* [[Wikipedia: Matrix factorization]] | * [[Wikipedia: Matrix factorization]] | ||
| + | * [https://sites.google.com/site/igorcarron2/matrixfactorizations The Matrix Factorization Jungle] | ||
[[Category: Method]] | [[Category: Method]] | ||
Latest revision as of 06:09, 24 September 2011
The approximation of a matrix by several lower-rank matrices is called matrix factorization. Matrix factorization methods are popular collaborative filtering methods, often with good scalability properties and predictive accuracy.
Literature
- Yehuda Koren, Robert Bell, Chris Volinsky: Matrix Factorization Techniques for Recommender Systems. IEEE Computer, 2009
- Rainer Gemulla, Peter Haas, Erik Nijkamp, Yannis Sismanis: Large-Scale Matrix Factorization with Distributed Stochastic Gradient Descent, KDD 2011
