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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Fairness-Aware Ranking in Search &amp; Recommendation Systems with Application to LinkedIn Talent Search · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1905.01989/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1905.01989/feed.xml" rel="self" type="application/rss+xml" /><item><title>Fairness-Aware Ranking in Search &amp; Recommendation Systems with Application to LinkedIn Talent Search — mentioned by publicdaniel</title><link>https://news.ycombinator.com/item?id=30056792</link><guid isPermaLink="false">urn:hackerpapers:mention:1905.01989:30056792</guid><pubDate>Mon, 24 Jan 2022 12:25:47 GMT</pubDate><description>&lt;p&gt;I work as a machine learning engineer on search &amp;amp; recommender systems, mostly focused on relevancy ranking in the recruiting space. It&amp;#x27;s unbelievable the pressure I have encountered to give &amp;quot;diverse&amp;quot; candidates a boost across the board in our scoring algorithms. Almost every client&amp;#x27;s talent acquisition team has asked what we can do to help them meet their diversity goals (which they loosely define as hiring more women and non-white / Asian people). I keep up with what some of the major players are doing (LinkedIn, Indeed, etc.), and they come straight out and say they preferentially weight…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1905.01989/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.IR</category></item></channel></rss>
