rrecsys: Environment for Evaluating Recommender Systems

Processes standard recommendation datasets (e.g., a user-item rating matrix) as input and generates rating predictions and lists of recommended items. Standard algorithm implementations which are included in this package are the following: Global/Item/User-Average baselines, Weighted Slope One, Item-Based KNN, User-Based KNN, FunkSVD, BPR and weighted ALS. They can be assessed according to the standard offline evaluation methodology (Shani, et al. (2011) <doi:10.1007/978-0-387-85820-3_8>) for recommender systems using measures such as MAE, RMSE, Precision, Recall, F1, AUC, NDCG, RankScore and coverage measures. The package (Coba, et al.(2017) <doi:10.1007/978-3-319-60042-0_36>) is intended for rapid prototyping of recommendation algorithms and education purposes.

Depends: R (≥ 3.1.2), registry, MASS, stats, knitr, ggplot2
Imports: methods, Rcpp
LinkingTo: Rcpp
Published: 2019-06-09
DOI: 10.32614/CRAN.package.rrecsys
Author: Ludovik Çoba [aut, cre, cph], Markus Zanker [ctb], Panagiotis Symeonidis [ctb]
Maintainer: Ludovik Çoba <Ludovik.Coba at inf.unibz.it>
BugReports: https://github.com/ludovikcoba/rrecsys/issues
License: GPL-3
URL: https://rrecsys.inf.unibz.it/
NeedsCompilation: yes
CRAN checks: rrecsys results


Reference manual: rrecsys.pdf
Vignettes: Introduction and Installing rrecsys
A data set in rrecsys
Non-personalized recommendations
Item-based k-nearest neighbors
User-based k-nearest neighbors
Simon Funk's SVD
Weighted Alternated Least Squares
Bayesian Personalized Ranking
Dispacher and registry
Predicting & recommending
Extendind rrecsys


Package source: rrecsys_0.
Windows binaries: r-devel: rrecsys_0., r-release: rrecsys_0., r-oldrel: rrecsys_0.
macOS binaries: r-release (arm64): rrecsys_0., r-oldrel (arm64): rrecsys_0., r-release (x86_64): rrecsys_0., r-oldrel (x86_64): rrecsys_0.
Old sources: rrecsys archive


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