<?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 Vonng</title><link>https://vonng.com/en/tags/recommendation-system/</link><description>Recent content in Recommendation System on Vonng</description><generator>Hugo -- gohugo.io</generator><language>en</language><managingEditor>rh@vonng.com (Ruohang Feng)</managingEditor><webMaster>rh@vonng.com (Ruohang Feng)</webMaster><copyright>© 2025 Ruohang Feng</copyright><lastBuildDate>Wed, 05 Apr 2017 00:00:00 +0000</lastBuildDate><atom:link href="https://vonng.com/en/tags/recommendation-system/index.xml" rel="self" type="application/rss+xml"/><item><title>Building an ItemCF Recommender in Pure SQL</title><link>https://vonng.com/en/pg/pg-recsys/</link><pubDate>Wed, 05 Apr 2017 00:00:00 +0000</pubDate><author>rh@vonng.com (Ruohang Feng)</author><guid>https://vonng.com/en/pg/pg-recsys/</guid><description>Five minutes, PostgreSQL, and the MovieLens dataset—that’s all you need to implement a classic item-based collaborative filtering recommender.</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://vonng.com/pg/pg-recsys/featured.jpg"/></item></channel></rss>