Project details for SVDFeature, A Toolkit for Informative Collaborative Filtering

Logo SVDFeature, A Toolkit for Informative Collaborative Filtering 1.1.3

by crowwork - October 7, 2011, 10:07:08 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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SVDFeature is a toolkit developed by Apex Data & Knowledge Management Lab during the competition of KDDCup'11. 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. Both learning to rank and rate prediction models are supported.

The toolkit is designed for facing large-scale dataset in CF problems. During the training process, we don't have load all the training data into main memory. A double thread pipeline technique is used to speed up the fetching of training data to make the model training efficient and memory frugal.

Changes to previous version:

Add detailed regularization settings, fix a bug that only occurs in windows

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
URL: Project Homepage
Supported Operating Systems: Linux, Windows, Unix
Data Formats: Ascii
Tags: Large Scale Learning, Collaborative Filtering, Contextual Aware Recommendation, Kddcup2011
Archive: download here

Other available revisons

Version Changelog Date

JMLR MLOSS version.

January 9, 2013, 02:21:18

Change the code path structure to allow easier inclusion of extensions. Start a new project homepage. Add the most recent version of extensions(though some are not yet documented) code used during KDDCup 2012.

July 24, 2012, 10:32:10

Add lite version of SVDFeature, used as example code for simple SGD implementation of the algorithm.

December 2, 2011, 12:14:48

Add support for common parameter space of user/item features. This allows interesting usage such as collective(joint) matrix factorization and symmetric relation prediction.

November 21, 2011, 08:21:52

No change in the toolkit, add a detailed non-trivial collaborative ranking experiment KDDCup'11 track2 using SVDFeature, getting state-of-art performance. The link to the experiment scripts are in our project page

November 2, 2011, 09:27:17

fix a bug happened in rank-model. More robust input buffering for user grouped format, less memory cost for loading input data when doing SVD++ and rank training.

October 12, 2011, 02:37:27

Add detailed regularization settings, fix a bug that only occurs in windows

October 7, 2011, 10:07:08

Add more regularization options

September 26, 2011, 05:00:47

The project is now self-contained. Add document of major interfaces in the project.

September 19, 2011, 12:30:07

Initial Announcement on

September 18, 2011, 07:18:39


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