Evaluation Metrics for Implicit-Feedback Recommender Systems R package recometrics version 0.1.6-3 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. Software 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 David Cortes <david.cortes.rivera@gmail.com> Comprehensive R Archive Network (CRAN) David Cortes BSD_2_clause + file LICENSE (https://CRAN.R-project.org/package=recometrics/LICENSE) 2023-02-19 application/tgz https://CRAN.R-project.org/package=recometrics doi:10.32614/CRAN.package.recometrics