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Modeling Online Behavior in Recommender Systems: The Importance of Temporal Context
Finally, we validate that the Pareto Fronts obtained with the added objective dominate those produced by state-of-the-art models that are only optimized for accuracy on three real-world publicly available datasets.
Momentum-based Gradient Methods in Multi-Objective Recommendation
We evaluate the benefits of Multi-objective Adamize on two multi-objective recommender systems and for three different objective combinations, both correlated or conflicting.
Addressing Fairness in Classification with a Model-Agnostic Multi-Objective Algorithm
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.
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