Collection of methods for rating matrix completion, which is a statistical framework for recommender systems. Another relevant application is the imputation of rating-scale survey data in the social and behavioral sciences. Note that matrix completion and imputation are synonymous terms used in different streams of the literature.
The main functionality implements robust matrix completion for discrete rating-scale data with a low-rank constraint on a latent continuous matrix. More information can be found in our paper:
Archimbaud, A., Alfons, A., and Wilms, I. (2025). Robust Matrix Completion for Discrete Rating-Scale Data. arXiv:2412.20802. doi:10.48550/arXiv.2412.20802.
In addition, the package provides wrapper functions for
softImpute (Mazumder,
Hastie, and Tibshirani, 2010; Hastie, Mazumder,
Lee, Zadeh, 2015) for easy tuning of the regularization parameter,
as well as benchmark methods such as median imputation and mode
imputation.
To install the latest version from GitHub, you can pull this
repository and install it from the R command line via
install.packages("devtools")
devtools::install_github("aalfons/RMCLab")If you already have package devtools installed, you can
skip the first line. Moreover, package RMCLab contains
C++ code that needs to be compiled, so you may need to
download and install the necessary tools for
MacOS or the necessary tools
for Windows.
If you experience any bugs or issues or if you have any suggestions for additional features, please submit an issue via the Issues tab of this repository. Please have a look at existing issues first to see if your problem or feature request has already been discussed.
If you want to contribute to the package, you can fork this repository and create a pull request after implementing the desired functionality.
If you need help using the package, or if you are interested in collaborations related to this project, please get in touch with the package maintainer.