recommenderlab: Lab for Developing and Testing Recommender Algorithms

Provides a research infrastructure to develop and evaluate collaborative filtering recommender algorithms. This includes a sparse representation for user-item matrices, many popular algorithms, top-N recommendations, and cross-validation. Hahsler (2022) <doi:10.48550/arXiv.2205.12371>.

Version: 1.0.0
Depends: R (≥ 3.5.0), Matrix, arules, proxy (≥ 0.4-26), registry
Imports: methods, utils, stats, irlba, recosystem, matrixStats
Suggests: testthat
Published: 2022-05-27
Author: Michael Hahsler ORCID iD [aut, cre, cph], Bregt Vereet [ctb]
Maintainer: Michael Hahsler <mhahsler at lyle.smu.edu>
BugReports: https://github.com/mhahsler/recommenderlab/issues
License: GPL-2
Copyright: (C) Michael Hahsler
URL: https://github.com/mhahsler/recommenderlab
NeedsCompilation: no
Classification/ACM: G.4, H.2.8
Citation: recommenderlab citation info
Materials: README NEWS
CRAN checks: recommenderlab results

Documentation:

Reference manual: recommenderlab.pdf
Vignettes: An introduction to the R package recommenderlab

Downloads:

Package source: recommenderlab_1.0.0.tar.gz
Windows binaries: r-devel: recommenderlab_0.2-7.zip, r-release: recommenderlab_0.2-7.zip, r-oldrel: recommenderlab_0.2-7.zip
macOS binaries: r-release (arm64): recommenderlab_0.2-7.tgz, r-oldrel (arm64): recommenderlab_0.2-7.tgz, r-release (x86_64): recommenderlab_0.2-7.tgz, r-oldrel (x86_64): recommenderlab_0.2-7.tgz
Old sources: recommenderlab archive

Reverse dependencies:

Reverse depends: recommenderlabBX, recommenderlabJester
Reverse suggests: cmfrec, crassmat, recometrics, RMOA

Linking:

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