Fairness measurement is crucial for assessing algorithmic bias in various types of machine
learning (ML) models, including ones used for search relevance, recommendation …

Industrial multimedia recommendation systems extensively utilize cascade architectures to
deliver personalized content for users, generally consisting of multiple stages like retrieval …

Sequential recommendation (SR) models are typically trained on user-item interactions
which are affected by the system exposure bias, leading to the user preference learned from …

In recent years, recommender systems have achieved remarkable performance by using
ensembles of heterogeneous models. However, this approach is costly due to the resources …

Although debiasing in multimedia recommendation has shown promising results, most
existing work relies on the ability of the model itself to fully disentangle the biased and …

Recommendation models trained on the user feedback collected from deployed
recommendation systems are commonly biased. User feedback is considerably affected by …

Often, recommendation systems employ continuous training, leading to a self-feedback loop
bias in which the system becomes biased toward its previous recommendations. Recent …