<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Sequential Recommendation on Chen Kai Blog</title><link>https://www.chenk.top/en/tags/sequential-recommendation/</link><description>Recent content in Sequential Recommendation on Chen Kai Blog</description><generator>Hugo</generator><language>en</language><lastBuildDate>Tue, 16 Dec 2025 09:00:00 +0000</lastBuildDate><atom:link href="https://www.chenk.top/en/tags/sequential-recommendation/index.xml" rel="self" type="application/rss+xml"/><item><title>Recommendation Systems (6): Sequential Recommendation and Session-based Modeling</title><link>https://www.chenk.top/en/recommendation-systems/06-sequential-recommendation/</link><pubDate>Tue, 16 Dec 2025 09:00:00 +0000</pubDate><guid>https://www.chenk.top/en/recommendation-systems/06-sequential-recommendation/</guid><description>&lt;p>When you scroll TikTok, every recommendation feels eerily on-point — not because the system reads your mind, but because it reads the &lt;strong>order&lt;/strong> of what you just watched. A cooking video followed by a travel vlog tells a different story than the same two clips in reverse. That ordering is exactly the signal that sequential recommenders are built to exploit.&lt;/p>
&lt;p>Compare two friends recommending shows. The first knows your favourite genres but never asks what you watched last week. The second says, &lt;em>&amp;ldquo;You just finished three sci-fi thrillers in a row — try this one.&amp;rdquo;&lt;/em> Traditional collaborative filtering is friend one. Sequential recommendation is friend two.&lt;/p></description></item></channel></rss>