<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Fairness on Abhiraj Bibhar</title><link>https://abhiraj.pages.dev/tags/fairness/</link><description>Recent content in Fairness on Abhiraj Bibhar</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sat, 01 Jun 2024 00:00:00 +0000</lastBuildDate><atom:link href="https://abhiraj.pages.dev/tags/fairness/index.xml" rel="self" type="application/rss+xml"/><item><title>Fairness-Aware Ranking in Multi-Stakeholder Recommender Systems</title><link>https://abhiraj.pages.dev/publications/fairness-aware-ranking-recommender-systems/</link><pubDate>Sat, 01 Jun 2024 00:00:00 +0000</pubDate><guid>https://abhiraj.pages.dev/publications/fairness-aware-ranking-recommender-systems/</guid><description>&lt;h2 id="abstract"&gt;Abstract&lt;/h2&gt;&#10;&lt;p&gt;Recommender systems increasingly serve multiple stakeholders — consumers, item providers, and platform operators — whose interests are not always aligned. We formalize a fairness-aware ranking objective that balances consumer relevance against provider-side exposure fairness, and propose an efficient re-ranking algorithm that achieves Pareto-improving trade-offs relative to relevance-only baselines on three large-scale recommendation datasets.&lt;/p&gt;</description></item></channel></rss>