Project details for SVDFeature, A Toolkit for Informative Collaborative Filtering

Logo SVDFeature, A Toolkit for Informative Collaborative Filtering 1.1.5

by crowwork - November 21, 2011, 08:21:52 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 2011. 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, neighbourhood 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 for both rate prediction and ranking. 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 support for common parameter space of user/item features. This allows interesting usage such as collective(joint) matrix factorization and symmetric relation prediction.

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
Supported Operating Systems: Linux, Windows, Unix
Data Formats: Ascii
Tags: Large Scale Learning, Collaborative Filtering, Collective Matrix Factorization, Context Aware Recommendation, Collaborative Ranking, Kddcup 2011
Archive: download here


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