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:	1.0.1
Depends:	R (≥ 3.5.0), Matrix, arules, proxy (≥ 0.4-26), registry
Imports:	methods, utils, stats, irlba, recosystem, matrixStats
Suggests:	testthat
Published:	2022-06-17
Author:	Michael Hahsler [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_1.0.1.tar.gz
Windows binaries:	r-devel: recommenderlab_1.0.1.zip, r-release: recommenderlab_1.0.1.zip, r-oldrel: recommenderlab_1.0.1.zip
macOS binaries:	r-release (arm64): recommenderlab_1.0.1.tgz, r-oldrel (arm64): recommenderlab_1.0.1.tgz, r-release (x86_64): recommenderlab_1.0.1.tgz, r-oldrel (x86_64): recommenderlab_1.0.1.tgz
Old sources:	recommenderlab archive

Reverse dependencies:

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

Linking:

Please use the canonical form https://CRAN.R-project.org/package=recommenderlab to link to this page.