<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Recommendation Sytem on DataThrillz</title><link>https://datathrillz.com/tags/recommendation-sytem/</link><description>Recent content in Recommendation Sytem on DataThrillz</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Mon, 07 Sep 2020 18:00:48 +0000</lastBuildDate><atom:link href="https://datathrillz.com/tags/recommendation-sytem/index.xml" rel="self" type="application/rss+xml"/><item><title>Building Recommendations Systems</title><link>https://datathrillz.com/posts/2020-09-07-building-recommendations-systems/</link><pubDate>Mon, 07 Sep 2020 18:00:48 +0000</pubDate><guid>https://datathrillz.com/posts/2020-09-07-building-recommendations-systems/</guid><description>&lt;p&gt;Recommendations systems are good for matching users to their favorite products and are incredibly popular. In fact you have likely used a recommendation system at least once in your life. For example, Amazon uses recommendation systems to suggest new exciting products to purchase based on users&amp;rsquo; previous purchase patterns and those similar users. Netflix also utilizes recommendation systems to suggest new TV Shows and movies.&lt;/p&gt;
&lt;p&gt;Before we get into recommendation systems, it is important to briefly cover two general-purpose approaches for identifying target customer groups and making product recommendations. These two approaches are called Clustering and Association Rules. &lt;/p&gt;</description></item></channel></rss>