Package: recometrics
Type: Package
Title: Evaluation Metrics for Implicit-Feedback Recommender Systems
Version: 0.1.6-3
Author: David Cortes
Maintainer: David Cortes <david.cortes.rivera@gmail.com>
URL: https://github.com/david-cortes/recometrics
BugReports: https://github.com/david-cortes/recometrics/issues
Description: 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.
LinkingTo: Rcpp, float
Imports: Rcpp (>= 1.0.1), Matrix (>= 1.3-4), MatrixExtra (>= 0.1.6),
        float, RhpcBLASctl, methods
Suggests: recommenderlab (>= 0.2-7), cmfrec (>= 3.2.0), data.table,
        knitr, rmarkdown, kableExtra, testthat
VignetteBuilder: knitr
License: BSD_2_clause + file LICENSE
RoxygenNote: 7.1.1
StagedInstall: TRUE
Biarch: TRUE
NeedsCompilation: yes
Repository: https://david-cortes.r-universe.dev
Date/Publication: 2025-05-09 18:25:03 UTC
RemoteUrl: https://github.com/david-cortes/recometrics
RemoteRef: HEAD
RemoteSha: 1edc56f5cf5fa00aef2bf1164e1bcc2427079702
Packaged: 2026-06-03 11:38:34 UTC; root