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rrecsys  

Environment for Evaluating Recommender Systems
View on CRAN: Click here


Download and install rrecsys package within the R console
Install from CRAN:
install.packages("rrecsys")

Install from Github:
library("remotes")
install_github("cran/rrecsys")

Install by package version:
library("remotes")
install_version("rrecsys", "0.9.7.3.1")



Attach the package and use:
library("rrecsys")
Maintained by
Ludovik Çoba
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2016-04-05
Latest Update: 2019-06-09
Description:
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) ) for recommender systems using measures such as MAE, RMSE, Precision, Recall, F1, AUC, NDCG, RankScore and coverage measures. The package (Coba, et al.(2017) ) is intended for rapid prototyping of recommendation algorithms and education purposes.
How to cite:
Ludovik Çoba (2016). rrecsys: Environment for Evaluating Recommender Systems. R package version 0.9.7.3.1, https://cran.r-project.org/web/packages/rrecsys. Accessed 27 Oct. 2026.
Previous versions and publish date:
(2026-07-09 06:53), 0.9.2 (2016-04-05 00:08), 0.9.5.1 (2016-06-14 20:34), 0.9.5.3 (2016-06-21 19:57), 0.9.5.4 (2016-06-27 17:31), 0.9.5 (2016-06-01 00:11), 0.9.7.1 (2017-08-18 17:24), 0.9.7.2 (2017-11-16 16:54), 0.9.7.3.1 (2019-06-09 20:45), 0.9.7.3 (2018-02-10 15:44)
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Complete documentation for rrecsys
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