Recommender System using Matrix Factorization
Yixuan Qiu, Chih-Jen Lin, Yu-Chin Juan, Yong Zhuang,
Wei-Sheng Chin and other contributors. See file AUTHORS for
details.
This package is an R wrapper of the libmf library
(http://www.csie.ntu.edu.tw/~cjlin/libmf/) for recommender
system using matrix factorization. It is typically used to
approximate an incomplete matrix using the product of two
matrices in a latent space. Other common names for this task
include "collaborative filtering", "matrix completion",
"matrix recovery", etc.

R wrapper of the 'libmf' library
(http://www.csie.ntu.edu.tw/~cjlin/libmf/) for recommender
system using matrix factorization. It is typically used to
approximate an incomplete matrix using the product of two
matrices in a latent space. Other common names for this task
include "collaborative filtering", "matrix completion",
"matrix recovery", etc.

Yixuan Qiu, Chih-Jen Lin, Yu-Chin Juan, Wei-Sheng Chin,
Yong Zhuang, Bo-Wen Yuan, Meng-Yuan Yang, and other
contributors. See file AUTHORS for details.
R wrapper of the 'libmf' library
(http://www.csie.ntu.edu.tw/~cjlin/libmf/) for recommender
system using matrix factorization. It is typically used to
approximate an incomplete matrix using the product of two
matrices in a latent space. Other common names for this task
include "collaborative filtering", "matrix completion",
"matrix recovery", etc. High performance multi-core parallel
computing is supported in this package.

R wrapper of the 'libmf' library
<http://www.csie.ntu.edu.tw/~cjlin/libmf/> for recommender
system using matrix factorization. It is typically used to
approximate an incomplete matrix using the product of two
matrices in a latent space. Other common names for this task
include "collaborative filtering", "matrix completion",
"matrix recovery", etc. High performance multi-core parallel
computing is supported in this package.

R wrapper of the 'libmf' library
<https://www.csie.ntu.edu.tw/~cjlin/libmf/> for recommender
system using matrix factorization. It is typically used to
approximate an incomplete matrix using the product of two
matrices in a latent space. Other common names for this task
include "collaborative filtering", "matrix completion",
"matrix recovery", etc. High performance multi-core parallel
computing is supported in this package.