wALSclass

wALSclass-class

show,wALSclass-method

Container for the model learned using any weighted Alternating Least Squares based algorithm.

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

wALSclass function

<dl class="dl-horizontal">
 <dt><code>alg</code>:</dt><dd>The algorithm denominator, of class <code>"character"</code>.</dd>
 <dt><code>data</code>:</dt><dd>the dataset used for training the model, class <code>"matrix"</code>.</dd>
 <dt><code>factors</code>:</dt><dd>user(U) and items(V) factors, class <code>"list"</code>.</dd>
 <dt><code>weightScheme</code>:</dt><dd>The weighting scheme used in updating the factors, class <code>"matrix"</code>.</dd>
 <dt><code>parameters</code>:</dt><dd>the parameters(such as number of factors <code>k</code>, learning rate <code>lambda</code>, number of iterations until <code>convergence</code> and the weighting scheme) used in the model, class <code>"list"</code>.</dd>
 </dl>

Slots

<dl class="dl-horizontal">
 <dt><code>show</code></dt><dd>signature(object = "wALSclass")</dd>
 </dl>

Methods

Weighted Alternating Least Squares based model. — wALSclass

<dl class='dl-horizontal'>
 <dt><code>alg</code>:</dt><dd>The algorithm denominator, of class <code>"character"</code>.</dd>
 <dt><code>data</code>:</dt><dd>the dataset used for training the model, class <code>"matrix"</code>.</dd>
 <dt><code>factors</code>:</dt><dd>user(U) and items(V) factors, class <code>"list"</code>.</dd>
 <dt><code>weightScheme</code>:</dt><dd>The weighting scheme used in updating the factors, class <code>"matrix"</code>.</dd>
 <dt><code>parameters</code>:</dt><dd>the parameters(such as number of factors <code>k</code>, learning rate <code>lambda</code>, number of iterations until <code>convergence</code> and the weighting scheme) used in the model, class <code>"list"</code>.</dd>
 </dl>

<dl class='dl-horizontal'>
 <dt><code>show</code></dt><dd>signature(object = "wALSclass")</dd>
 </dl>

wALSclass: Weighted Alternating Least Squares based model.

Description

Arguments

Slots

Methods

See Also