<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Multi-Objective on Chen Kai Blog</title><link>https://www.chenk.top/en/tags/multi-objective/</link><description>Recent content in Multi-Objective on Chen Kai Blog</description><generator>Hugo</generator><language>en</language><lastBuildDate>Thu, 25 Dec 2025 09:00:00 +0000</lastBuildDate><atom:link href="https://www.chenk.top/en/tags/multi-objective/index.xml" rel="self" type="application/rss+xml"/><item><title>Recommendation Systems (9): Multi-Task Learning and Multi-Objective Optimization</title><link>https://www.chenk.top/en/recommendation-systems/09-multi-task-learning/</link><pubDate>Thu, 25 Dec 2025 09:00:00 +0000</pubDate><guid>https://www.chenk.top/en/recommendation-systems/09-multi-task-learning/</guid><description>&lt;p>A live e-commerce ranker doesn&amp;rsquo;t optimize just one number. The same model that decides which product to show you also predicts, in the same forward pass, whether you will click, add it to your cart, pay for it, return it, or leave a positive review. Each prediction is a different &lt;em>task&lt;/em> with its own data distribution, scarcity, and incentives. These tasks are tightly coupled: a clicker is more likely to convert, a converter is more likely to write a review, and a high-CTR thumbnail can attract clicks that reduce watch time.&lt;/p></description></item></channel></rss>