Difference between revisions of "SVDFeature"
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| − | '''SVDFeature''' is a | + | '''SVDFeature''' is a toolkitdesigned 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 [[KDD_Cup_2011|KDDCup'11]]. | Using the toolkit, we built the best single model reported in track 1 [[KDD_Cup_2011|KDDCup'11]]. | ||
Revision as of 05:47, 23 September 2011
SVDFeature is a toolkitdesigned 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.
External links
- Project page at mloss: http://mloss.org/software/view/333/
- Project homepage: http://apex.sjtu.edu.cn/apex_wiki/svdfeature
