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rrecsys (version 0.9.5.4)

Environment for Assessing Recommender Systems

Description

Provides implementations of several popular recommendation systems. They can process standard recommendation datasets (user/item matrix) as input and generate rating predictions and recommendation lists. Standard algorithm implementations included in this package are: Global/Item/User-Average baselines, Item-Based KNN, FunkSVD, BPR and weighted ALS. They can be assessed according to the standard offline evaluation methodology for recommender systems using measures such as MAE, RMSE, Precision, Recall, AUC, NDCG, RankScore and coverage measures. The package is intended for rapid prototyping of recommendation algorithms and education purposes.

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install.packages('rrecsys')

Monthly Downloads

263

Version

0.9.5.4

License

GPL-3

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Maintainer

Ludovik oba

Last Published

June 27th, 2016

Functions in rrecsys (0.9.5.4)

dataSet-class

Dataset class.
evalRec

Evaluates the requested recommendation algorithm.
evalModel

Creating the evaluation model.
algAverageClass

Baseline algorithms exploiting global/item and user averages.
IBclass

Item based model.
evalPred

Evaluates the requested prediction algorithm.
evalModel-class

Evaluation model.
BPRclass

Bayesian Personalized Ranking based model.
getAUC

Returns the Area under the ROC curve.
defineData

Define dataset.
recResultsClass

Results of a recommendation.
SVDclass

SVD model.
setStoppingCriteria

Set stopping criteria.
mlLatest100k

Movielens Latest
recommend

Generate recommendation.
PPLclass

Popularity based model.
wALSclass

Weighted Alternating Least Squares based model.
predict

Generate predictions.
rrecsys

Create a recommender system.
nDCG

Normalized Discounted Cumulative Gain
rankScore

Rank Score