28 citations · 59 across the 8 of their papers we have counts for
8 papers
Unlocking LLMs: Addressing Scarce Data and Bias Challenges in Mental Health
Vivek Kumar, Eirini Ntoutsi, Pushpraj Singh Rajawat +2
Large language models (LLMs) have shown promising capabilities in healthcare analysis but face several challenges like hallucinations, parroting, and bias manifestation. These chal…
How Fair is Your Diffusion Recommender Model?
Daniele Malitesta, Giacomo Medda, Erasmo Purificato +3
Diffusion-based learning has settled as a rising paradigm in generative recommendation, outperforming traditional approaches built upon variational autoencoders and generative adve…
Fair Augmentation for Graph Collaborative Filtering
Ludovico Boratto, Francesco Fabbri, Gianni Fenu +2
Recent developments in recommendation have harnessed the collaborative power of graph neural networks (GNNs) in learning users' preferences from user-item networks. Despite emergin…
Robustness in Fairness against Edge-level Perturbations in GNN-based Recommendation
Ludovico Boratto, Francesco Fabbri, Gianni Fenu +2
Efforts in the recommendation community are shifting from the sole emphasis on utility to considering beyond-utility factors, such as fairness and robustness. Robustness of recomme…
Counterfactual Graph Augmentation for Consumer Unfairness Mitigation in Recommender Systems
Ludovico Boratto, Francesco Fabbri, Gianni Fenu +2
In recommendation literature, explainability and fairness are becoming two prominent perspectives to consider. However, prior works have mostly addressed them separately, for insta…
GNNUERS: Fairness Explanation in GNNs for Recommendation via Counterfactual Reasoning
Giacomo Medda, Francesco Fabbri, Mirko Marras +2
Nowadays, research into personalization has been focusing on explainability and fairness. Several approaches proposed in recent works are able to explain individual recommendations…
Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations
Ludovico Boratto, Gianni Fenu, Mirko Marras +1
Enabling non-discrimination for end-users of recommender systems by introducing consumer fairness is a key problem, widely studied in both academia and industry. Current research h…
Improving Fairness in Speaker Recognition
Gianni Fenu, Giacomo Medda, Mirko Marras +1
The human voice conveys unique characteristics of an individual, making voice biometrics a key technology for verifying identities in various industries. Despite the impressive pro…