Difference between revisions of "SVDFeature"

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== Key ideas of SVDFeature ==
 
== Key ideas of SVDFeature ==
 
* SVDFeature is not a toolkit implementing different kinds specific matrix factorization models. Maybe it's not the best choice for users who's looking for a ready-to-useimplementation for  a specific algorithm. Some other softwares(e.g [[MyMediaLite]]) may be a better choice.
 
* SVDFeature is not a toolkit implementing different kinds specific matrix factorization models. Maybe it's not the best choice for users who's looking for a ready-to-useimplementation for  a specific algorithm. Some other softwares(e.g [[MyMediaLite]]) may be a better choice.
* SVDFeature is like a generic toolkit for developing new algorithms by defining features. If you want to try to research new algorithms for contextual aware recommendation or composite some existing models together( such as SVD++, neighborhood ), SVDFeature shall be a better choice, since you only need to write script for feature generation, and new model can be learned using SVDFeature.
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* SVDFeature is like a generic toolkit for developing new algorithms by defining features. If you want to try to research new algorithms for contextual aware recommendation or composite some existing models together( such as SVD++, neighborhood ), you may want to use SVDFeature, since you only need to write script for feature generation, and new model can be learned using SVDFeature.
  
  

Revision as of 21:23, 23 September 2011

SVDFeature is a toolkit designed to solve the feature-based matrix factorization efficiently. Unlike traditional engineering approaches for collaborative filtering which requires writing specific code for each algorithm, SVDFeature develop new models just by defining new features. The feature-based setting allows us to include many kinds of information into the model, making the model informative. Using the toolkit, we can easily incorporate information such as temporal dynamics, neighborhood relationship, and hierarchical information into the model. Besides rating prediction, it is also capable of doing pairwise ranking tasks for item prediction.

Using the toolkit, we built the best single model reported in track 1 KDDCup'11. SVDFeature is distributed under apache-2.0.

Key ideas of SVDFeature

  • SVDFeature is not a toolkit implementing different kinds specific matrix factorization models. Maybe it's not the best choice for users who's looking for a ready-to-useimplementation for a specific algorithm. Some other softwares(e.g MyMediaLite) may be a better choice.
  • SVDFeature is like a generic toolkit for developing new algorithms by defining features. If you want to try to research new algorithms for contextual aware recommendation or composite some existing models together( such as SVD++, neighborhood ), you may want to use SVDFeature, since you only need to write script for feature generation, and new model can be learned using SVDFeature.


External links