<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>DCN V2 on Yiwei Shi</title><link>/tags/dcn-v2/</link><description>Recent content in DCN V2 on Yiwei Shi</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Tue, 12 May 2026 08:21:39 -0400</lastBuildDate><atom:link href="/tags/dcn-v2/index.xml" rel="self" type="application/rss+xml"/><item><title>Two-Tower, DCN v2, and Transformers: How Modern Retrieval and Ranking Fit Together</title><link>/posts/20260512-modern-sequence-modelling/</link><pubDate>Tue, 12 May 2026 08:21:39 -0400</pubDate><guid>/posts/20260512-modern-sequence-modelling/</guid><description>&lt;p&gt;If you&amp;rsquo;ve spent any time around modern recommendation, search, or ads systems, you&amp;rsquo;ve run into three architectures that keep showing up: &lt;strong&gt;two-tower models&lt;/strong&gt;, &lt;strong&gt;DCN v2&lt;/strong&gt;, and &lt;strong&gt;Transformers&lt;/strong&gt;. They&amp;rsquo;re often discussed as if they&amp;rsquo;re alternatives, but in production they&amp;rsquo;re almost always &lt;em&gt;composed&lt;/em&gt;. Each one solves a different problem, and the interesting design work is in how you fit them together.&lt;/p&gt;
&lt;p&gt;This post walks through what each does, where they slot in, and how a typical large-scale retrieval-and-ranking stack actually uses all three.&lt;/p&gt;</description></item></channel></rss>