SVDFeature - Revision history

Tqchen at 02:09, 12 December 2012

2012-12-12T02:09:44Z

Tqchen: /* External links */

2012-12-12T02:08:41Z

External links

Zeno Gantner: Category: CPlusPlus

2012-02-09T15:52:28Z

Category: CPlusPlus

Tqchen: /* Usage Examples */

2011-12-05T08:48:54Z

Usage Examples

Tqchen: /* Usage Examples */

2011-11-02T09:01:41Z

Usage Examples

Tqchen at 08:59, 2 November 2011

2011-11-02T08:59:56Z

Zeno Gantner: wikification, mention other toolkits, license link (hope that's okay ;-)

2011-09-26T13:43:54Z

wikification, mention other toolkits, license link (hope that's okay ;-)

Tqchen at 02:05, 24 September 2011

2011-09-24T02:05:22Z

Tqchen at 01:28, 24 September 2011

2011-09-24T01:28:07Z

Tqchen at 01:23, 24 September 2011

2011-09-24T01:23:09Z

@@ Line 3: / Line 3: @@
 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]].
+Using the toolkit, we built the best single model reported in track 1 [[KDD Cup 2011]], and champion of track1 [[KDD Cup 2012]]
 SVDFeature is distributed under the [http://www.apache.org/licenses/LICENSE-2.0.html Apache License, Version 2.0].
 == Features ==
-* Large-scale data handling: The toolkit buffers the training data on disk thus memory cost is invariant to training data size. For track 1 of [[KDD Cup 2011]], SVDFeature trains a very complex model using less than 2G memory.
+* Big data handling: The toolkit buffers the training data on disk thus memory cost is invariant to training data size. For track 1 of [[KDD Cup 2011]], SVDFeature trains a very complex model using less than 2G memory.
 * Strong description ability: Many variants of [[matrix factorization]] can be described in feature-based matrix factorization. One can try new approaches by generating corresponding features, and no modification of code is required.
@@ Line 13: / Line 13: @@
 * SVDFeature is not a toolkit implementing many specific matrix factorization models. Maybe it is not the best choice for users who are looking for a ready-to-use implementation of a specific algorithm. Some other toolkits (e.g [[LensKit]], [[Mahout]], or [[MyMediaLite]]) may be a better choice.
 * SVDFeature is a generic toolkit for developing new algorithms by defining features. If you want to research new algorithms for [[context-aware recommendation]] or compose some existing models together (such as [[SVD++]], [[neighborhood-based models]]), you may want to use SVDFeature, since you only need to write a script for feature generation, and the new model can be learned using SVDFeature.
 == External links ==

@@ Line 20: / Line 20: @@
 == External links ==
-* Project Homepage: http://apex.sjtu.edu.cn/apex_wiki/svdfeature
+* Project Homepage: http://svdfeature.apexlab.org
 * Project at mloss.org: http://mloss.org/software/view/333/
 [[Category: CPlusPlus]]
 [[Category: Software]]

@@ Line 22: / Line 22: @@
 * Project Homepage: http://apex.sjtu.edu.cn/apex_wiki/svdfeature
 * Project at mloss.org: http://mloss.org/software/view/333/
 [[Category: Software]]

@@ Line 16: / Line 16: @@
 == Usage Examples ==
 * The toolkit provide a demo folder with example scripts.
-* A non-trivial experiment on KDDCup'11 track2 dataset using SVDFeature, getting 3.1x error rate: http://apex.sjtu.edu.cn/apex_wiki/kddtrack2
+* A non-trivial experiment on KDDCup 2011 track1 dataset using SVDFeature: http://apex.sjtu.edu.cn/apex_wiki/kddtrack1
 == External links ==

@@ Line 16: / Line 16: @@
 == Usage Examples ==
 * The toolkit provide a demo folder with example scripts.
-* A non-trivial experiment on KDDCup'11 track2 dataset using SVDFeature, getting 3.1x error: http://apex.sjtu.edu.cn/apex_wiki/kddtrack2
+* A non-trivial experiment on KDDCup'11 track2 dataset using SVDFeature, getting 3.1x error rate: http://apex.sjtu.edu.cn/apex_wiki/kddtrack2
 == External links ==

@@ Line 6: / Line 6: @@
 SVDFeature is distributed under apache-2.0.
-== Key ideas of SVDFeature ==
+== Features of Toolkit ==
 * SVDFeature is not a toolkit implementing many 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.

@@ Line 8: / Line 8: @@
 == 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 ), SVDFeature shall be a better choice, since you only need to write script for feature generation, and new model can be learned using SVDFeature.
+* 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.