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cs.IR2024
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…
cs.IR2024
How to Diversify any Personalized Recommender?
Manel Slokom, Savvina Danil, Laura Hollink
In this paper, we introduce a novel approach to improve the diversity of Top-N recommendations while maintaining accuracy. Our approach employs a user-centric pre-processing strate…