16 citations · 29 across the 7 of their papers we have counts for
7 papers
PONTE: Personalized Orchestration for Natural Language Trustworthy Explanations
Vittoria Vineis, Matteo Silvestri, Lorenzo Antonelli +2
Explainable Artificial Intelligence (XAI) seeks to enhance the transparency and accountability of machine learning systems, yet most methods follow a one-size-fits-all paradigm tha…
Demystifying Sequential Recommendations: Counterfactual Explanations via Genetic Algorithms
Domiziano Scarcelli, Filippo Betello, Giuseppe Perelli +2
Sequential Recommender Systems (SRSs) have demonstrated remarkable effectiveness in capturing users' evolving preferences. However, their inherent complexity as "black box" models…
One Search Fits All: Pareto-Optimal Eco-Friendly Model Selection
Filippo Betello, Antonio Purificato, Vittoria Vineis +2
The environmental impact of Artificial Intelligence (AI) is emerging as a significant global concern, particularly regarding model training. In this paper, we introduce GREEN (Guid…
The Role of Fake Users in Sequential Recommender Systems
Filippo Betello
Sequential Recommender Systems (SRSs) are widely used to model user behavior over time, yet their robustness remains an under-explored area of research. In this paper, we conduct a…
A Reproducible Analysis of Sequential Recommender Systems
Filippo Betello, Antonio Purificato, Federico Siciliano +4
Sequential Recommender Systems (SRSs) have emerged as a highly efficient approach to recommendation systems. By leveraging sequential data, SRSs can identify temporal patterns in u…
Attention-Map Augmentation for Hypercomplex Breast Cancer Classification
Eleonora Lopez, Filippo Betello, Federico Carmignani +2
Breast cancer is the most widespread neoplasm among women and early detection of this disease is critical. Deep learning techniques have become of great interest to improve diagnos…