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Calculates evaluation metrics for implicit-feedback recommender systems that are based on low-rank matrix factorization models, given the fitted model matrices and data, thus allowing to compare models from a variety of libraries. Metrics include P@K (precision-at-k, for top-K recommendations), R@K (recall at k), AP@K (average precision at k), NDCG@K (normalized discounted cumulative gain at k), Hit@K (from which the 'Hit Rate' is calculated), RR@K (reciprocal rank at k, from which the 'MRR' or 'mean reciprocal rank' is calculated), ROC-AUC (area under the receiver-operating characteristic curve), and PR-AUC (area under the precision-recall curve). These are calculated on a per-user basis according to the ranking of items induced by the model, using efficient multi-threaded routines. Also provides functions for creating train-test splits for model fitting and evaluation.
| Version: | 0.1.6-3 |
| Imports: | Rcpp (≥ 1.0.1), Matrix (≥ 1.3-4), MatrixExtra (≥ 0.1.6), float, RhpcBLASctl, methods |
| LinkingTo: | Rcpp, float |
| Suggests: | recommenderlab (≥ 0.2-7), cmfrec (≥ 3.2.0), data.table, knitr, rmarkdown, kableExtra, testthat |
| Published: | 2023-02-19 |
| Author: | David Cortes |
| Maintainer: | David Cortes <david.cortes.rivera at gmail.com> |
| BugReports: | https://github.com/david-cortes/recometrics/issues |
| License: | BSD_2_clause + file LICENSE |
| URL: | https://github.com/david-cortes/recometrics |
| NeedsCompilation: | yes |
| CRAN checks: | recometrics results |
| Reference manual: | recometrics.pdf |
| Vignettes: |
Evaluating_recommender_systems |
| Package source: | recometrics_0.1.6-3.tar.gz |
| Windows binaries: | r-devel: recometrics_0.1.6-3.zip, r-release: recometrics_0.1.6-3.zip, r-oldrel: recometrics_0.1.6-3.zip |
| macOS binaries: | r-release (arm64): recometrics_0.1.6-3.tgz, r-oldrel (arm64): recometrics_0.1.6-3.tgz, r-release (x86_64): recometrics_0.1.6-3.tgz, r-oldrel (x86_64): recometrics_0.1.6-3.tgz |
| Old sources: | recometrics archive |
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