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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "recometrics" in publications use:'
type: software
license: BSD-2-Clause
title: 'recometrics: Evaluation Metrics for Implicit-Feedback Recommender Systems'
version: 0.1.6-3
doi: 10.32614/CRAN.package.recometrics
abstract: 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.
authors:
- family-names: Cortes
  given-names: David
  email: david.cortes.rivera@gmail.com
repository: https://cran.r-universe.dev
repository-code: https://github.com/david-cortes/recometrics
commit: 05fdda4a9a147a4ae82f23c08bf90430a6475478
url: https://github.com/david-cortes/recometrics
date-released: '2023-02-19'
contact:
- family-names: Cortes
  given-names: David
  email: david.cortes.rivera@gmail.com