5 citations · 9 across the 6 of their papers we have counts for
8 papers
"It's Not Just Hate'': A Multi-Dimensional Perspective on Detecting Harmful Speech Online
Federico Bianchi, Stefanie Anja Hills, Patricia Rossini +3
Well-annotated data is a prerequisite for good Natural Language Processing models. Too often, though, annotation decisions are governed by optimizing time or annotator agreement. W…
Designing Explanations for Group Recommender Systems
A. Felfernig, N. Tintarev, T. N. T. Trang +1
Explanations are used in recommender systems for various reasons. Users have to be supported in making (high-quality) decisions more quickly. Developers of recommender systems want…
Disparate Impact Diminishes Consumer Trust Even for Advantaged Users
Tim Draws, Zoltán Szlávik, Benjamin Timmermans +3
Systems aiming to aid consumers in their decision-making (e.g., by implementing persuasive techniques) are more likely to be effective when consumers trust them. However, recent re…
Operationalizing Framing to Support Multiperspective Recommendations of Opinion Pieces
Mats Mulder, Oana Inel, Jasper Oosterman +1
Diversity in personalized news recommender systems is often defined as dissimilarity, and based on topic diversity (e.g., corona versus farmers strike). Diversity in news media, ho…
Assessing Viewpoint Diversity in Search Results Using Ranking Fairness Metrics
Tim Draws, Nava Tintarev, Ujwal Gadiraju +2
The way pages are ranked in search results influences whether the users of search engines are exposed to more homogeneous, or rather to more diverse viewpoints. However, this viewp…
Contextual Personalized Re-Ranking of Music Recommendations through Audio Features
Boning Gong, Mesut Kaya, Nava Tintarev
Users are able to access millions of songs through music streaming services like Spotify, Pandora, and Deezer. Access to such large catalogs, created a need for relevant song recom…