<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Paper | Interactive Intelligent Systems Group</title><link>https://interintsys.github.io/tag/paper/</link><atom:link href="https://interintsys.github.io/tag/paper/index.xml" rel="self" type="application/rss+xml"/><description>Paper</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Fri, 17 Apr 2026 00:00:00 +0000</lastBuildDate><image><url>https://interintsys.github.io/media/sharing.png</url><title>Paper</title><link>https://interintsys.github.io/tag/paper/</link></image><item><title>Hat-trick at UMAP 2026</title><link>https://interintsys.github.io/news/20260415_3umappapers/</link><pubDate>Fri, 17 Apr 2026 00:00:00 +0000</pubDate><guid>https://interintsys.github.io/news/20260415_3umappapers/</guid><description>&lt;p>&lt;strong>Three full papers&lt;/strong> have been accepted at the upcoming A-rated &lt;a href="https://www.um.org/umap2026/" target="_blank" rel="noopener">UMAP 2026&lt;/a> conference:&lt;/p>
&lt;ul>
&lt;li>
&lt;p>&lt;strong>Large-scale comparison of diversification methods in news recommendation&lt;/strong> in collaboration with &lt;a href="https://www.dpgmediagroup.com" target="_blank" rel="noopener">DPG Media&lt;/a>: Co-authored by Robin Verachtert and Kim Falk (&lt;a href="https://www.dpgmediagroup.com" target="_blank" rel="noopener">DPG Media&lt;/a>) and &lt;a href="https://interintsys.github.io/authors/christine-bauer/">Christine Bauer&lt;/a>,
this paper reports a large-scale comparison of three diversification methods deployed on &lt;a href="https://www.nu.nl/" target="_blank" rel="noopener">NU.nl&lt;/a> (a Dutch online newspaper)&amp;mdash;interleaving and two intra-list diversification (ILD) variants (TF-IDF, BERT). In the deployment on &lt;a href="https://www.nu.nl/" target="_blank" rel="noopener">NU.nl&lt;/a>, ILD with BERT reduced intra-list similarity, increased click-through rates, and improved perceived relevance&amp;mdash;aligning diversification with editorial goals.&lt;/p>
&lt;/li>
&lt;li>
&lt;p>&lt;strong>Modeling preference heterogeneity&lt;/strong>: Led by &lt;a href="https://interintsys.github.io/authors/anouk-van-kasteren/">Anouk van Kasteren&lt;/a>,
supervised by Judith Masthoff and &lt;a href="https://interintsys.github.io/authors/christine-bauer/">Christine Bauer&lt;/a>, and in collaboration with &lt;a href="https://interintsys.github.io/authors/marloes-vredenborg/">Marloes Vredenborg&lt;/a>,
this paper models preference heterogeneity for context-aware decision support during public transport disruptions, exploring how situational factors shape preferences and choices under real-world conditions.&lt;/p>
&lt;/li>
&lt;li>
&lt;p>&lt;strong>Study on tone in recommendation explanations&lt;/strong>: Led by &lt;a href="https://interintsys.github.io/authors/marloes-vredenborg/">Marloes Vredenborg&lt;/a>,
supervised by Judith Masthoff, Marit Bentvelzen, and &lt;a href="https://interintsys.github.io/authors/christine-bauer/">Christine Bauer&lt;/a>,
this paper shows how situational context&amp;mdash;especially urgency&amp;mdash;shapes user preferences for explanation tone. It introduces a generalizable methodology for studying and designing context- and tone-aware explanations and, in the public-transport domain, offers qualitative insights into users’ needs and a validated set of explanation tones.&lt;/p>
&lt;/li>
&lt;/ul>
&lt;br>
&lt;div class="pub-list-item view-citation" style="margin-bottom: 1rem">
&lt;i class="far fa-file-alt pub-icon" aria-hidden="true">&lt;/i>
&lt;span class="article-metadata li-cite-author">
&lt;span >
&lt;a href="https://interintsys.github.io/authors/robin-verachtert/">Robin Verachtert&lt;/a>&lt;/span>, &lt;span >
&lt;a href="https://interintsys.github.io/authors/kim-falk/">Kim Falk&lt;/a>&lt;/span>, &lt;span class="author-highlighted">
&lt;a href="https://interintsys.github.io/authors/christine-bauer/">Christine Bauer&lt;/a>&lt;/span>
&lt;/span>
(2026).
&lt;a href="https://interintsys.github.io/publications/verachtert-2026-nunl/">Enhancing diversity in news recommendations increases click-through rates: Insights from an online experiment and user study&lt;/a>.
&lt;em>34th ACM International Conference on User Modeling, Adaptation and Personalization&lt;/em> (UMAP 2026).
&lt;p>
&lt;a href="#" class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal"
data-filename="/publications/verachtert-2026-nunl/cite.bib">
Cite
&lt;/a>
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://doi.org/10.1145/3774935.3806153" target="_blank" rel="noopener">
DOI
&lt;/a>
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://dl.acm.org/doi/10.1145/3774935.3806153?cid=81453628934" target="_blank" rel="noopener">
&lt;i class="ai ai-acmdl">&lt;/i>
ACM Author-izer
&lt;/a>
&lt;span class="btn btn-outline-primary btn-page-header btn-sm jif">ICORE 2026: A &lt;/span>
&lt;span class="btn btn-outline-primary btn-page-header btn-sm jif">GGS 2021: B &lt;/span>
&lt;span data-badge-popover="right" data-badge-type="donut"
data-doi="10.1145/3774935.3806153"
data-hide-no-mentions="true" class="altmetric-embed altmetric-style">&lt;/span>
&lt;/p>
&lt;/div>
&lt;div class="pub-list-item view-citation" style="margin-bottom: 1rem">
&lt;i class="far fa-file-alt pub-icon" aria-hidden="true">&lt;/i>
&lt;span class="article-metadata li-cite-author">
&lt;span class="author-highlighted">
&lt;a href="https://interintsys.github.io/authors/anouk-van-kasteren/">Anouk van Kasteren&lt;/a>&lt;/span>, &lt;span class="author-highlighted">
&lt;a href="https://interintsys.github.io/authors/marloes-vredenborg/">Marloes Vredenborg&lt;/a>&lt;/span>, &lt;span class="author-highlighted">
&lt;a href="https://interintsys.github.io/authors/christine-bauer/">Christine Bauer&lt;/a>&lt;/span>, &lt;span >
&lt;a href="https://interintsys.github.io/authors/judith-masthoff/">Judith Masthoff&lt;/a>&lt;/span>
&lt;/span>
(2026).
&lt;a href="https://interintsys.github.io/publications/vankasteren-2026-modelling/">Modelling preference heterogeneity for context-aware decision support during public transport disruptions&lt;/a>.
&lt;em>34th ACM International Conference on User Modeling, Adaptation and Personalization&lt;/em> (UMAP 2026).
&lt;p>
&lt;a href="#" class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal"
data-filename="/publications/vankasteren-2026-modelling/cite.bib">
Cite
&lt;/a>
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://doi.org/10.1145/3774935.3806191" target="_blank" rel="noopener">
DOI
&lt;/a>
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://dl.acm.org/doi/10.1145/3774935.3806191?cid=81453628934" target="_blank" rel="noopener">
&lt;i class="ai ai-acmdl">&lt;/i>
ACM Author-izer
&lt;/a>
&lt;span class="btn btn-outline-primary btn-page-header btn-sm jif">ICORE 2026: A &lt;/span>
&lt;span class="btn btn-outline-primary btn-page-header btn-sm jif">GGS 2021: B &lt;/span>
&lt;span data-badge-popover="right" data-badge-type="donut"
data-doi="10.1145/3774935.3806191"
data-hide-no-mentions="true" class="altmetric-embed altmetric-style">&lt;/span>
&lt;/p>
&lt;/div>
&lt;div class="pub-list-item view-citation" style="margin-bottom: 1rem">
&lt;i class="far fa-file-alt pub-icon" aria-hidden="true">&lt;/i>
&lt;span class="article-metadata li-cite-author">
&lt;span class="author-highlighted">
&lt;a href="https://interintsys.github.io/authors/marloes-vredenborg/">Marloes Vredenborg&lt;/a>&lt;/span>, &lt;span >
&lt;a href="https://interintsys.github.io/authors/marit-bentvelzen/">Marit Bentvelzen&lt;/a>&lt;/span>, &lt;span class="author-highlighted">
&lt;a href="https://interintsys.github.io/authors/christine-bauer/">Christine Bauer&lt;/a>&lt;/span>, &lt;span >
&lt;a href="https://interintsys.github.io/authors/judith-masthoff/">Judith Masthoff&lt;/a>&lt;/span>
&lt;/span>
(2026).
&lt;a href="https://interintsys.github.io/publications/vredenborg-2026-doestonematter/">Does tone matter? Exploring context-aware explanations in route recommendations&lt;/a>.
&lt;em>34th ACM International Conference on User Modeling, Adaptation and Personalization&lt;/em> (UMAP 2026).
&lt;p>
&lt;a href="#" class="btn btn-outline-primary btn-page-header btn-sm js-cite-modal"
data-filename="/publications/vredenborg-2026-doestonematter/cite.bib">
Cite
&lt;/a>
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://doi.org/10.1145/3774935.3806780" target="_blank" rel="noopener">
DOI
&lt;/a>
&lt;a class="btn btn-outline-primary btn-page-header btn-sm" href="https://dl.acm.org/doi/10.1145/3774935.3806780?cid=81453628934" target="_blank" rel="noopener">
&lt;i class="ai ai-acmdl">&lt;/i>
ACM Author-izer
&lt;/a>
&lt;span class="btn btn-outline-primary btn-page-header btn-sm jif">ICORE 2026: A &lt;/span>
&lt;span class="btn btn-outline-primary btn-page-header btn-sm jif">GGS 2021: B &lt;/span>
&lt;span data-badge-popover="right" data-badge-type="donut"
data-doi="10.1145/3774935.3806780"
data-hide-no-mentions="true" class="altmetric-embed altmetric-style">&lt;/span>
&lt;/p>
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