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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:1.0.7
Depends:R (≥ 4.5.0),Matrix,arules (≥ 1.7-11),proxy (≥ 0.4-26)
Imports:registry, methods, utils, stats,irlba,recosystem,matrixStats
Suggests:testthat
Published:2025-05-31
DOI:10.32614/CRAN.package.recommenderlab
Author:Michael HahslerORCID 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.html ,recommenderlab.pdf
Vignettes:An introduction to the R package recommenderlab (source,R code)

Downloads:

Package source: recommenderlab_1.0.7.tar.gz
Windows binaries: r-devel:recommenderlab_1.0.7.zip, r-release:recommenderlab_1.0.7.zip, r-oldrel:recommenderlab_1.0.6.zip
macOS binaries: r-release (arm64):recommenderlab_1.0.7.tgz, r-oldrel (arm64):recommenderlab_1.0.6.tgz, r-release (x86_64):recommenderlab_1.0.7.tgz, r-oldrel (x86_64):recommenderlab_1.0.6.tgz
Old sources: recommenderlab archive

Reverse dependencies:

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

Linking:

Please use the canonical formhttps://CRAN.R-project.org/package=recommenderlabto link to this page.


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