MovieLensToy

This dataset is a toy example derived from the MovieLens 100K dataset. The 
original data were collected via the MovieLens website (movielens.umn.edu) 
over a seven-month period, from September 19, 1997, to April 22, 1998.

datasets

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.

Andreas Alfons

RMCLab

Lab for Matrix Completion and Imputation of Discrete Rating Data

Aurore Archimbaud

MovieLensToy function

A matrix with 149 rows and 33 variables. Rows represent users and 
columns respresent movies. Each cell contains a rating from 1 to 5,
or <code>NA</code> if the movie was not rated by the user.
The row names correspond to the user ID and the column names to the movie ID 
in the original MovieLens 100K dataset.

MovieLensToy: Toy example derived from the MovieLens 100K dataset

Description

Usage

Arguments

Format

Details

References

Examples