Lab for Matrix Completion and Imputation of Discrete Rating Data
Create splits of observed data cells for hyperparameter tuning
Extract the completed (imputed) data matrix
Extract the optimal value of the regularization parameter
Extract the number of iterations
Construct grid of values for the regularization parameter
Median imputation
Mode imputation
Robust discrete matrix completion with hyperparameter tuning
Robust discrete matrix completion
tools:::Rd_package_title("RMCLab")
Matrix completion via nuclear-norm regularization with hyperparameter ...
Matrix completion via nuclear-norm regularization
Control objects for hyperparameter validation
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 (Archimbaud, Alfons, and Wilms (2025) <doi:10.48550/arXiv.2412.20802>). In addition, the package provides wrapper functions for 'softImpute' (Mazumder, Hastie, and Tibshirani, 2010, <https://www.jmlr.org/papers/v11/mazumder10a.html>; Hastie, Mazumder, Lee, Zadeh, 2015, <https://www.jmlr.org/papers/v16/hastie15a.html>) for easy tuning of the regularization parameter, as well as benchmark methods such as median imputation and mode imputation.