<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Recommender Systems on Christian Hotz-Behofsits</title><link>https://hotzbehofsits.com/tags/recommender-systems/</link><description>Recent content in Recommender Systems on Christian Hotz-Behofsits</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 26 Oct 2019 20:59:29 +0200</lastBuildDate><atom:link href="https://hotzbehofsits.com/tags/recommender-systems/index.xml" rel="self" type="application/rss+xml"/><item><title>Recommender Systems in R</title><link>https://hotzbehofsits.com/post/recommender-systems-r/</link><pubDate>Sat, 26 Oct 2019 20:59:29 +0200</pubDate><guid>https://hotzbehofsits.com/post/recommender-systems-r/</guid><description>&lt;p&gt;A former WU-member, Michael Hahsler, created a really nice package called recommenderlab, which allows you to build collaborative filtering systems. But before you can use it, you have to install all required packages:&lt;/p&gt;
&lt;pre tabindex="0"&gt;&lt;code&gt;install.packages(c(&amp;#34;recommenderlab&amp;#34;, &amp;#34;dplyr&amp;#34;, &amp;#34;readr&amp;#34;))
&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;&amp;hellip; and load them:&lt;/p&gt;
&lt;div class="highlight"&gt;&lt;pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;-webkit-text-size-adjust:none;"&gt;&lt;code class="language-r" data-lang="r"&gt;&lt;span style="display:flex;"&gt;&lt;span&gt;&lt;span style="color:#a6e22e"&gt;library&lt;/span&gt;(recommenderlab)
&lt;/span&gt;&lt;/span&gt;&lt;span style="display:flex;"&gt;&lt;span&gt;&lt;span style="color:#a6e22e"&gt;library&lt;/span&gt;(tibble)
&lt;/span&gt;&lt;/span&gt;&lt;span style="display:flex;"&gt;&lt;span&gt;&lt;span style="color:#a6e22e"&gt;library&lt;/span&gt;(dplyr)
&lt;/span&gt;&lt;/span&gt;&lt;span style="display:flex;"&gt;&lt;span&gt;&lt;span style="color:#a6e22e"&gt;library&lt;/span&gt;(readr)
&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;p&gt;I asked my students to answer some questions. One question was about their favourite TV series, which is a good example for a recommender system &lt;em&gt;(Netflix for example does pretty much the same)&lt;/em&gt;. So they filled out this survey and I generated a table by adding a new row for each student and a new column for each TV show. I put a 1 in the cell if the user likes the show and nothing if not. You can import such a table &lt;em&gt;(CSV-format)&lt;/em&gt; using the following command &lt;em&gt;(all columns are by default parsed as integers except user, which is a string)&lt;/em&gt;. The file itself came from a course survey and is not published here, so you will have to substitute your own:&lt;/p&gt;</description></item></channel></rss>