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.
Version: 0.9.7.3.1 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 Ãoba [aut, cre, cph], Markus Zanker [ctb], Panagiotis Symeonidis [ctb] Maintainer: Ludovik Ãoba <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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=rrecsys to link to this page.
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