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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: 1.0.6
Depends: R (≥ 3.5.0), Matrix, arules, proxy (≥ 0.4-26)
Imports: registry, methods, utils, stats, irlba, recosystem, matrixStats
Suggests: testthat
Published: 2023-09-20
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_1.0.6.tar.gz
Windows binaries: r-devel: recommenderlab_1.0.6.zip, r-release: recommenderlab_1.0.6.zip, r-oldrel: recommenderlab_1.0.6.zip
macOS binaries: r-release (arm64): recommenderlab_1.0.6.tgz, r-oldrel (arm64): recommenderlab_1.0.6.tgz, r-release (x86_64): recommenderlab_1.0.6.tgz, r-oldrel (x86_64): recommenderlab_1.0.6.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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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