Giacomo Medda

Giacomo Medda

Postdoc @ UniCA • RecSys • Fairness in AI

Explainability · Recommender Systems · Graph Neural Networks · Fairness · Robustness

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

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Blog Post number 4

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Blog Post number 3

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Blog Post number 2

less than 1 minute read

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Blog Post number 1

less than 1 minute read

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portfolio

projects

Robustness in Fairness against Edge-level Perturbations in GNN-based Recommendation

Published:

Motivation

Why study robustness in fairness? While recommendation robustness typically focuses on maintaining utility under attacks, little research explores how attacks affect fairness. An attacker could exploit this blind spot to compromise a system's fairness without significantly changing overall accuracy—potentially damaging a company's reputation and violating emerging regulations.

Our work addresses the intersection of two critical properties: robustness (resilience to attacks) and fairness (equitable treatment across demographic groups). We investigate whether GNN-based recommender systems can maintain fair outcomes when subjected to edge-level perturbations.

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Unlocking LLMs: Addressing Scarce Data and Bias Challenges in Mental Health

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Motivation

Why focus on MI and LLMs? Motivational Interviewing (MI) is a proven therapeutic technique for behavioral change, but access is limited due to cost and clinician availability. LLMs could help democratize access, but they face critical challenges in sensitive healthcare domains: hallucinations, stochastic parroting, and bias manifestation.

Mental health domains suffer from data scarcity—there are few publicly available resources that could help develop responsible AI systems. This work addresses this gap by creating high-quality synthetic MI dialogues using LLMs and rigorous expert annotation.

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Fair Augmentation for Graph Collaborative Filtering

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Motivation

Why study fairness-aware graph augmentation? Graph Neural Networks (GNNs) have become the state-of-the-art for collaborative filtering. However, unfairness issues in graph collaborative filtering remain underexplored, especially from the consumer's perspective. A notable gap exists in the formalization and evaluation of fairness mitigation algorithms on cutting-edge GNN models.

This paper serves as a solid response to recent research highlighting unfairness issues by:

  • Reproducing a state-of-the-art fair graph augmentation method
  • Evaluating across an extensive setup: 11 GNNs, 5 non-GNN models, 5 real-world networks
  • Investigating the transferability of fair augmented graphs to new models

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Small Data, Big Impact: Navigating Resource Limitations in Point-of-Interest Recommendation for Individuals with Autism

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Motivation

Why POI Recommendation for ASD? Autism Spectrum Disorder affects sensory perception, making spatial exploration challenging and anxiety-inducing. Digital technologies, including recommender systems, can assist ASD users by suggesting Points of Interest aligned with their sensory preferences. However, this represents a low-resource problem due to demographic constraints and engagement difficulties.

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How Fair is Your Diffusion Recommender Model?

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Motivation

Why study fairness in diffusion recommenders? Diffusion models represent a new generation of recommender systems with state-of-the-art performance. However, their fairness properties are completely unknown. Understanding potential biases is crucial before widespread deployment of these systems.

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GreenFoodLens: Sustainability Labels for Food Recommendation

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Motivation

Why sustainability in food recommendation? Food production accounts for over 26% of global greenhouse gas emissions. Food recommender systems could promote environmentally-conscious choices, but existing datasets lack environmental impact information. GreenFoodLens bridges this gap by enriching the largest food recommendation corpus with sustainability labels.

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publications

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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