6 citations · 11 across the 3 of their papers we have counts for
3 papers
Modeling Online Behavior in Recommender Systems: The Importance of Temporal Context
Milena Filipovic, Blagoj Mitrevski, Diego Antognini +3
Recommender systems research tends to evaluate model performance offline and on randomly sampled targets, yet the same systems are later used to predict user behavior sequentially…
Momentum-based Gradient Methods in Multi-Objective Recommendation
Blagoj Mitrevski, Milena Filipovic, Diego Antognini +3
Multi-objective gradient methods are becoming the standard for solving multi-objective problems. Among others, they show promising results in developing multi-objective recommender…
Addressing Fairness in Classification with a Model-Agnostic Multi-Objective Algorithm
Kirtan Padh, Diego Antognini, Emma Lejal Glaude +2
The goal of fairness in classification is to learn a classifier that does not discriminate against groups of individuals based on sensitive attributes, such as race and gender. One…