28 citations · 113 across the 27 of their papers we have counts for
9 papers · 1 filter
OKRA: an Explainable, Heterogeneous, Multi-Stakeholder Job Recommender System
Roan Schellingerhout, Francesco Barile, Nava Tintarev
The use of recommender systems in the recruitment domain has been labeled as 'high-risk' in recent legislation. As a result, strict requirements regarding explainability and fairne…
De-centering the (Traditional) User: Multistakeholder Evaluation of Recommender Systems
Robin Burke, Gediminas Adomavicius, Toine Bogers +7
Multistakeholder recommender systems are those that account for the impacts and preferences of multiple groups of individuals, not just the end users receiving recommendations. Due…
Navigating the Thin Line: Examining User Behavior in Search to Detect Engagement and Backfire Effects
F. M. Cau, N. Tintarev
Opinionated users often seek information that aligns with their preexisting beliefs while dismissing contradictory evidence due to confirmation bias. This conduct hinders their abi…
Explaining Search Result Stances to Opinionated People
Z. Wu, T. Draws, F. Cau +3
People use web search engines to find information before forming opinions, which can lead to practical decisions with different levels of impact. The cognitive effort of search can…
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…
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…
Ephemeral Context to Support Robust and Diverse Music Recommendations
Pavel Kucherbaev, Nava Tintarev, Carlos Rodriguez
While prior work on context-based music recommendation focused on fixed set of contexts (e.g. walking, driving, jogging), we propose to use multiple sensors and external data sourc…