no code implementations • 12 Mar 2024 • Andreas Damianou, Francesco Fabbri, Paul Gigioli, Marco De Nadai, Alice Wang, Enrico Palumbo, Mounia Lalmas
In the realm of personalization, integrating diverse information sources such as consumption signals and content-based representations is becoming increasingly critical to build state-of-the-art solutions.
no code implementations • 8 Mar 2024 • Marco De Nadai, Francesco Fabbri, Paul Gigioli, Alice Wang, Ang Li, Fabrizio Silvestri, Laura Kim, Shawn Lin, Vladan Radosavljevic, Sandeep Ghael, David Nyhan, Hugues Bouchard, Mounia Lalmas-Roelleke, Andreas Damianou
While promising, this move presents significant challenges for personalized recommendations.
1 code implementation • 24 Jan 2024 • Ludovico Boratto, Francesco Fabbri, Gianni Fenu, Mirko Marras, Giacomo Medda
Efforts in the recommendation community are shifting from the sole emphasis on utility to considering beyond-utility factors, such as fairness and robustness.
no code implementations • 14 Sep 2023 • Francesco Fabbri, Xianghang Liu, Jack R. McKenzie, Bartlomiej Twardowski, Tri Kurniawan Wijaya
Federated Learning (FL) has emerged as a key approach for distributed machine learning, enhancing online personalization while ensuring user data privacy.
1 code implementation • 23 Aug 2023 • Ludovico Boratto, Francesco Fabbri, Gianni Fenu, Mirko Marras, Giacomo Medda
In recommendation literature, explainability and fairness are becoming two prominent perspectives to consider.
1 code implementation • 12 Apr 2023 • Giacomo Medda, Francesco Fabbri, Mirko Marras, Ludovico Boratto, Gianni Fenu
Moreover, an empirical evaluation of the perturbed network uncovered relevant patterns that justify the nature of the unfairness discovered by the generated explanations.
no code implementations • 5 Jan 2023 • Yanhao Wang, Michael Mathioudakis, Jia Li, Francesco Fabbri
Diversity maximization aims to select a diverse and representative subset of items from a large dataset.
1 code implementation • 30 Jul 2022 • Yanhao Wang, Francesco Fabbri, Michael Mathioudakis
Given a set $X$ of $n$ elements, it asks to select a subset $S$ of $k \ll n$ elements with maximum \emph{diversity}, as quantified by the dissimilarities among the elements in $S$.
1 code implementation • 1 Feb 2022 • Francesco Fabbri, Yanhao Wang, Francesco Bonchi, Carlos Castillo, Michael Mathioudakis
Hence, we define the problem of reducing the prevalence of radicalization pathways by selecting a small number of edges to "rewire", so to minimize the maximum of segregation scores among all radicalized nodes, while maintaining the relevance of the recommendations.
1 code implementation • 9 Oct 2020 • Yanhao Wang, Francesco Fabbri, Michael Mathioudakis
We study the problem of extracting a small subset of representative items from a large data stream.