<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Recommender Systems | Yiqiao Jin CS PhD @ Georgia Tech</title><link>https://ahren09.github.io/tags/recommender-systems/</link><atom:link href="https://ahren09.github.io/tags/recommender-systems/index.xml" rel="self" type="application/rss+xml"/><description>Recommender Systems</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Sun, 30 Apr 2023 00:00:00 +0000</lastBuildDate><image><url>https://ahren09.github.io/media/icon_hu_eee6347cbdb2cc3f.png</url><title>Recommender Systems</title><link>https://ahren09.github.io/tags/recommender-systems/</link></image><item><title>Code Recommendation for Open Source Project Developers</title><link>https://ahren09.github.io/publication/www23_coder/</link><pubDate>Sun, 30 Apr 2023 00:00:00 +0000</pubDate><guid>https://ahren09.github.io/publication/www23_coder/</guid><description>&lt;h2 id="abstract">Abstract&lt;/h2>
&lt;p>Open Source Software (OSS) developers contribute to a large and rapidly growing ecosystem, but matching developers to relevant code is challenging. We introduce CODER, a graph-based code recommendation framework that leverages multi-modal signals from OSS contribution and engagement networks.&lt;/p>
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