LIBMF: A Matrix-factorization Library for Recommender Systems by Machine Learning Group at National Taiwan University.
From the webpage:
LIBMF is an open source tool for approximating an incomplete matrix using the product of two matrices in a latent space. Matrix factorization is commonly used in collaborative filtering. Main features of LIBMF include
- In addition to the latent user and item features, we add user bias, item bias, and average terms for better performance.
- LIBMF can be parallelized in a multi-core machine. To make our package more efficient, we use SSE instructions to accelerate the vector product operations.
For a data sets of 250M ratings, LIBMF takes less then eight minutes to converge to a reasonable level.
Download
The current release (Version 1.0, Sept 2013) of LIBMF can be obtained by downloading the zip file or tar.gz file.
Please read the COPYRIGHT notice before using LIBMF.
Documentation
The algorithms of LIBMF is described in the following paper.
Y. Zhuang, W.-S. Chin, Y.-C. Juan, and C.-J. Lin. A Fast Parallel SGD for Matrix Factorization in Shared Memory Systems. Proceedings of ACM Recommender Systems 2013.
See README in the package for the practical use.
Being curious about what “practical use” would be in the README, ;-), I discovered a demo data set. And basic instructions for use.
For the details of application for recommendations, see the paper.