3 papers
cs.IR2025
FedFlex: Federated Learning for Diverse Netflix Recommendations
Sven Lankester, Gustavo de Carvalho Bertoli, Matias Vizcaino +2
The drive for personalization in recommender systems creates a tension between user privacy and the risk of "filter bubbles". Although federated learning offers a promising paradig…
cs.IR2025
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…
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…