Description:

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:

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.

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Linux, Windows, Unix

Data Formats: Ascii

Tags: Large Scale Learning, Collaborative Filtering, Contextual Aware Recommendation, Kddcup2011

Archive: download here

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