8 citations · 16 across the 5 of their papers we have counts for
5 papers
Human, Algorithm, or Both? Gender Bias in Human-Augmented Recruiting
Mesut Kaya, Toine Bogers
Recent years have seen rapid growth in the market for HR technology and AI-driven HR solutions in particular. This popularity has also resulted in increased attention to the negati…
Mapping Stakeholder Needs to Multi-Sided Fairness in Candidate Recommendation for Algorithmic Hiring
Mesut Kaya, Toine Bogers
Already before the enactment of the EU AI Act, candidate or job recommendation for algorithmic hiring -- semi-automatically matching CVs to job postings -- was used as an example o…
Recommenders with a mission: assessing diversity in newsrecommendations
Sanne Vrijenhoek, Mesut Kaya, Nadia Metoui +3
News recommenders help users to find relevant online content and have the potential to fulfill a crucial role in a democratic society, directing the scarce attention of citizens to…
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…
Sudden Death: A New Way to Compare Recommendation Diversification
Derek Bridge, Mesut Kaya, Pablo Castells
This paper describes problems with the current way we compare the diversity of different recommendation lists in offline experiments. We illustrate the problems with a case study.…