6 citations · 7 across the 4 of their papers we have counts for
9 papers
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…
User Modeling and User Profiling: A Comprehensive Survey
Erasmo Purificato, Ludovico Boratto, Ernesto William De Luca
The integration of artificial intelligence (AI) into daily life, particularly through information retrieval and recommender systems, has necessitated advanced user modeling and pro…
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…
A Cost-Sensitive Meta-Learning Strategy for Fair Provider Exposure in Recommendation
Ludovico Boratto, Giulia Cerniglia, Mirko Marras +2
When devising recommendation services, it is important to account for the interests of all content providers, encompassing not only newcomers but also minority demographic groups.…
MOReGIn: Multi-Objective Recommendation at the Global and Individual Levels
Elizabeth Gómez, David Contreras, Ludovico Boratto +1
Multi-Objective Recommender Systems (MORSs) emerged as a paradigm to guarantee multiple (often conflicting) goals. Besides accuracy, a MORS can operate at the global level, where a…
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…