_ds-class

show,_ds-method

summary,_ds-method

rowAverages

colAverages

rowRatings

colRatings

numRatings

sparsity

sparsity,_ds-method

Defines a structure for a dataset that distinguishes between binary and non-binary feedback datasets.

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.

Ludovik <c3><87>oba

rrecsys

Environment for Evaluating Recommender Systems

Markus Zanker

Panagiotis Symeonidis

_ds-class function

<dl class="dl-horizontal">
 <dt><code>binary</code>:</dt><dd>class <code>"logical"</code>, determines if the item dataset contains binary (i.e. 1/0) or non-binary ratings.</dd>
 <dt><code>minimum</code>:</dt><dd>class <code>"numeric"</code>, defines the minimal value present in the dataset.</dd>
 <dt><code>maximum</code>:</dt><dd>class <code>"numeric"</code>, defines the maximal value present in the dataset.</dd>
 <dt><code>intScale</code>:</dt><dd>object of class <code>"logical"</code>, if TRUE the range of ratings in the dataset contains as well half star values.</dd>
 </dl>

Slots

<dl class="dl-horizontal">
 <dt>show</dt><dd>signature(object = "_ds")</dd>
 <dt>sparsity</dt><dd>signature(object = "_ds"): returns the sparsity of the dataset.</dd>
 <dt>summary</dt><dd>signature(object = "_ds"): summary of the characteristics of the dataset.</dd>
 </dl>

Methods

Dataset class. — _ds-class

<dl class='dl-horizontal'>
 <dt><code>binary</code>:</dt><dd>class <code>"logical"</code>, determines if the item dataset contains binary (i.e. 1/0) or non-binary ratings.</dd>
 <dt><code>minimum</code>:</dt><dd>class <code>"numeric"</code>, defines the minimal value present in the dataset.</dd>
 <dt><code>maximum</code>:</dt><dd>class <code>"numeric"</code>, defines the maximal value present in the dataset.</dd>
 <dt><code>intScale</code>:</dt><dd>object of class <code>"logical"</code>, if TRUE the range of ratings in the dataset contains as well half star values.</dd>
 </dl>

<dl class='dl-horizontal'>
 <dt>show</dt><dd>signature(object = "_ds")</dd>
 <dt>sparsity</dt><dd>signature(object = "_ds"): returns the sparsity of the dataset.</dd>
 <dt>summary</dt><dd>signature(object = "_ds"): summary of the characteristics of the dataset.</dd>
 </dl>

_ds-class: Dataset class.

Description

Arguments

Slots

Methods