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Sparse Linear Method(SLIM) predicts ratings and top-n recommendations suited for sparse implicit positive feedback systems. SLIM is decomposed into multiple elasticnet optimization problems which are solved in parallel over multiple cores. The package is based on "SLIM: Sparse Linear Methods for Top-N Recommender Systems" by Xia Ning and George Karypis <doi:10.1109/ICDM.2011.134>.
| Version: | 0.1.0 |
| Depends: | R (≥ 3.3.3), stats (≥ 3.3.3) |
| Imports: | assertthat (≥ 0.1), parallel (≥ 3.3.3), Matrix (≥ 1.2.8), glmnet (≥ 2.0.5), bigmemory (≥ 4.5.19), pbapply (≥ 1.3.2) |
| Published: | 2017-03-25 |
| DOI: | 10.32614/CRAN.package.slimrec |
| Author: | Srikanth KS [aut, cre] |
| Maintainer: | Srikanth KS <sri.teach at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | slimrec results |
| Reference manual: | slimrec.pdf |
| Package source: | slimrec_0.1.0.tar.gz |
| Windows binaries: | r-devel: slimrec_0.1.0.zip, r-release: slimrec_0.1.0.zip, r-oldrel: slimrec_0.1.0.zip |
| macOS binaries: | r-release (arm64): slimrec_0.1.0.tgz, r-oldrel (arm64): slimrec_0.1.0.tgz, r-release (x86_64): slimrec_0.1.0.tgz, r-oldrel (x86_64): slimrec_0.1.0.tgz |
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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.