recometrics: Evaluation Metrics for Implicit-Feedback Recommender Systems
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
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.
||Rcpp (≥ 1.0.1), Matrix (≥ 1.3-4), MatrixExtra (≥ 0.1.6), float, RhpcBLASctl, methods
||recommenderlab (≥ 0.2-7), cmfrec (≥ 3.2.0), data.table, knitr, rmarkdown, kableExtra, testthat
||David Cortes <david.cortes.rivera at gmail.com>
||BSD_2_clause + file LICENSE
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