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) ) 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.


Reference manual

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install.packages("rrecsys") by Ludovik Çoba, a month ago


Report a bug at https://github.com/ludovikcoba/rrecsys/issues

Browse source code at https://github.com/cran/rrecsys

Authors: Ludovik Çoba [aut, cre, cph], Markus Zanker [ctb], Panagiotis Symeonidis [ctb]

Documentation:   PDF Manual  

GPL-3 license

Imports methods, Rcpp

Depends on registry, MASS, stats, knitr, ggplot2

Linking to Rcpp

See at CRAN