MatrixFactorizationRecommender

All matrix factorization recommenders inherit from this class

internal

Implements many algorithms for statistical learning on
sparse matrices - matrix factorizations, matrix completion,
elastic net regressions, factorization machines.
Also 'rsparse' enhances 'Matrix' package by providing methods for
multithreaded <sparse, dense> matrix products and native slicing of
the sparse matrices in Compressed Sparse Row (CSR) format.
List of the algorithms for regression problems:
1) Elastic Net regression via Follow The Proximally-Regularized Leader (FTRL)
Stochastic Gradient Descent (SGD), as per McMahan et al(, <doi:10.1145/2487575.2488200>)
2) Factorization Machines via SGD, as per Rendle (2010, <doi:10.1109/ICDM.2010.127>)
List of algorithms for matrix factorization and matrix completion:
1) Weighted Regularized Matrix Factorization (WRMF) via Alternating Least
Squares (ALS) - paper by Hu, Koren, Volinsky (2008, <doi:10.1109/ICDM.2008.22>)
2) Maximum-Margin Matrix Factorization via ALS, paper by Rennie, Srebro
(2005, <doi:10.1145/1102351.1102441>)
3) Fast Truncated Singular Value Decomposition (SVD), Soft-Thresholded SVD,
Soft-Impute matrix completion via ALS - paper by Hastie, Mazumder
et al. (2014, <arXiv:1410.2596>)
4) Linear-Flow matrix factorization, from 'Practical linear models for
large-scale one-class collaborative filtering' by Sedhain, Bui, Kawale et al
(2016, ISBN:978-1-57735-770-4)
5) GlobalVectors (GloVe) matrix factorization via SGD, paper by Pennington,
Socher, Manning (2014, <https://www.aclweb.org/anthology/D14-1162>)
Package is reasonably fast and memory efficient - it allows to work with large
datasets - millions of rows and millions of columns. This is particularly useful
for practitioners working on recommender systems.

Dmitriy Selivanov

rsparse

Statistical Learning on Sparse Matrices

Drew Schmidt

Wei-Chen Chen

MatrixFactorizationRecommender function

<dl class="dl-horizontal">
<dt><code>components</code></dt><dd>item embeddings</dd>
</dl>

Public fields

<h3>Public methods</h3><ul>
<li><a href="/link/MatrixFactorizationRecommender%24predict()?package=rsparse&version=0.4.0" data-mini-rdoc="rsparse::MatrixFactorizationRecommender$predict()"><code>MatrixFactorizationRecommender$predict()</code></a></li>
<li><a href="/link/MatrixFactorizationRecommender%24clone()?package=rsparse&version=0.4.0" data-mini-rdoc="rsparse::MatrixFactorizationRecommender$clone()"><code>MatrixFactorizationRecommender$clone()</code></a></li>
</ul>
<h3>Method <code>predict()</code></h3>recommends items for users
<h3>Usage</h3><pre>MatrixFactorizationRecommender$predict(
 x,
 k,
 not_recommend = x,
 items_exclude = integer(0),
 ...
)</pre><h3>Arguments</h3><dl class="dl-horizontal">
<dt><code>x</code></dt><dd>user-item interactions matrix (usually sparse - `Matrix::sparseMatrix`).Users are
rows and items are columns</dd></dl><dt><code>k</code></dt><dd>number of items to recommend</dd><dt><code>not_recommend</code></dt><dd>user-item matrix (sparse) which describes which items method should NOT
recomment for each user. Usually this is same as `x` as we don't want to recommend items user
already liked.</dd><dt><code>items_exclude</code></dt><dd>either integer indices or character identifiers of the items to not
recommend to any user.</dd><dt><code>...</code></dt><dd>not used at the moment</dd>
<h3>Method <code>clone()</code></h3>The objects of this class are cloneable with this method.
<h3>Usage</h3><pre>MatrixFactorizationRecommender$clone(deep = FALSE)</pre><h3>Arguments</h3><dl class="dl-horizontal">
<dt><code>deep</code></dt><dd>Whether to make a deep clone.</dd>
</dl>

Methods

Base class for matrix factorization recommenders — MatrixFactorizationRecommender

<dl class='dl-horizontal'>
<dt><code>components</code></dt><dd>item embeddings</dd>
</dl>

MatrixFactorizationRecommender: Base class for matrix factorization recommenders

Description

Arguments

Public fields

Methods

Public methods

Method `predict()`

Usage

Arguments

Method `clone()`

Usage

Arguments

Description

Arguments

Public fields

Methods

Public methods

Method predict()

Usage

Arguments

Method clone()

Usage

Arguments

Method `predict()`

Method `clone()`