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cs.IR2026
Adaptive Quality-Diversity Trade-offs for Large-Scale Batch Recommendation
Clémence Réda, Tomas Rigaux, Hiba Bederina +3
A core research question in recommender systems is to propose batches of highly relevant and diverse items, that is, items personalized to the user's preferences, but which also mi…
cs.IR2025
Diversified recommendations of cultural activities with personalized determinantal point processes
Carole Ibrahim, Hiba Bederina, Daniel Cuesta +3
While optimizing recommendation systems for user engagement is a well-established practice, effectively diversifying recommendations without negatively impacting core business metr…
cs.IR2025
Bayesian-Guided Diversity in Sequential Sampling for Recommender Systems
Hiba Bederina, Jill-Jênn Vie
The challenge of balancing user relevance and content diversity in recommender systems is increasingly critical amid growing concerns about content homogeneity and reduced user eng…