<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Recommendation System on PIGSTY</title><link>https://pigsty.io/tags/recommendation-system/</link><description>Recent content in Recommendation System on PIGSTY</description><generator>Hugo</generator><language>en</language><lastBuildDate>Fri, 17 Apr 2026 09:14:36 +0800</lastBuildDate><atom:link href="https://pigsty.io/tags/recommendation-system/index.xml" rel="self" type="application/rss+xml"/><item><title>Building an ItemCF Recommender in Pure SQL</title><link>https://pigsty.io/blog/pg/pg-recsys/</link><pubDate>Wed, 05 Apr 2017 00:00:00 +0000</pubDate><guid>https://pigsty.io/blog/pg/pg-recsys/</guid><description>&lt;p&gt;&lt;a href="https://vonng.com/en/pg/pg-recsys/"&gt;&lt;img src="https://pigsty.io/img/hero/pg/pg-recsys.jpg" alt="featured"&gt;&lt;/a&gt;&lt;/p&gt;
&lt;p&gt;Five minutes, PostgreSQL, and the MovieLens dataset—that’s all you need to implement a classic item-based collaborative filtering recommender. &lt;a href="https://vonng.com/en/pg/pg-recsys/"&gt;&lt;strong&gt;Read more&lt;/strong&gt;&lt;/a&gt;&lt;/p&gt;</description></item></channel></rss>