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recometrics
View on CRAN: Click
here
Download and install recometrics package within the R console
Install from CRAN:
install.packages("recometrics")
Install from Github:
library("remotes")
install_github("cran/recometrics") Install by package version:
library("remotes")
install_version("recometrics", "0.1.6-3") Attach the package and use:
library("recometrics")
Maintained by
David Cortes
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-07-15
Latest Update: 2023-02-19
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.
How to cite:
David Cortes (2021). recometrics: Evaluation Metrics for Implicit-Feedback Recommender Systems. R package version 0.1.6-3, https://cran.r-project.org/web/packages/recometrics. Accessed 08 Oct. 2026.
Previous versions and publish date:
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imports, suggests or enhances
Complete documentation for recometrics
Functions, R codes and Examples using
the recometrics R package
Some associated functions: calc.reco.metrics . create.reco.train.test .
Some associated R codes: RcppExports.R . recometrics.R . Full recometrics package functions and examples
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