28 citations · 59 across the 8 of their papers we have counts for
4 papers · 1 filter
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…