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.

Getting started

Package details

AuthorLudovik Çoba [aut, cre, cph], Markus Zanker [ctb], Panagiotis Symeonidis [ctb]
MaintainerLudovik Çoba <Ludovik.Coba@inf.unibz.it>
URL https://rrecsys.inf.unibz.it/
Package repositoryView on CRAN
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rrecsys documentation built on June 10, 2019, 1:02 a.m.