# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- 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://david-cortes.r-universe.dev repository-code: https://github.com/david-cortes/recometrics commit: 7fb0244b99b046eff94125c0cecd167391b7e942 url: https://github.com/david-cortes/recometrics contact: - family-names: Cortes given-names: David email: david.cortes.rivera@gmail.com