13 citations · 15 across the 3 of their papers we have counts for
3 papers
On the challenges of studying bias in Recommender Systems: A UserKNN case study
Savvina Daniil, Manel Slokom, Mirjam Cuper +3
Statements on the propagation of bias by recommender systems are often hard to verify or falsify. Research on bias tends to draw from a small pool of publicly available datasets an…
Diversity of What? On the Different Conceptualizations of Diversity in Recommender Systems
Sanne Vrijenhoek, Savvina Daniil, Jorden Sandel +1
Diversity is a commonly known principle in the design of recommender systems, but also ambiguous in its conceptualization. Through semi-structured interviews we explore how practit…
Hidden Author Bias in Book Recommendation
Savvina Daniil, Mirjam Cuper, Cynthia C. S. Liem +2
Collaborative filtering algorithms have the advantage of not requiring sensitive user or item information to provide recommendations. However, they still suffer from fairness relat…