Difference between revisions of "SVDFeature"
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(Created page with "'''SVDFeature''' is a toolkit to solve recommendation problem using feature-based matrix factorization. It is designed to solve the feature-based matrix factorization efficiently...") |
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'''SVDFeature''' is a toolkit to solve recommendation problem using feature-based matrix factorization. It is designed to solve the feature-based matrix factorization efficiently. New models can be developed 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]]. | '''SVDFeature''' is a toolkit to solve recommendation problem using feature-based matrix factorization. It is designed to solve the feature-based matrix factorization efficiently. New models can be developed 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 [[ | + | Using the toolkit, we built the best single model reported in track 1 [[KDD_Cup_2011|KDDCup'11]]. |
SVDFeature is distributed under apache-2.0. | SVDFeature is distributed under apache-2.0. | ||
Revision as of 05:42, 23 September 2011
SVDFeature is a toolkit to solve recommendation problem using feature-based matrix factorization. It is designed to solve the feature-based matrix factorization efficiently. New models can be developed 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.
External links
- Project page at mloss: http://mloss.org/software/view/333/
- Project homepage: http://apex.sjtu.edu.cn/apex_wiki/svdfeature
