136 citations · 412 across the 29 of their papers we have counts for
13 papers · 1 filter
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…
Faithful Path Language Modeling for Explainable Recommendation over Knowledge Graph
Giacomo Balloccu, Ludovico Boratto, Christian Cancedda +2
The integration of path reasoning with language modeling in recommender systems has shown promise for enhancing explainability but often struggles with the authenticity of the expl…
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…
GNNUERS: Fairness Explanation in GNNs for Recommendation via Counterfactual Reasoning
Giacomo Medda, Francesco Fabbri, Mirko Marras +2
Nowadays, research into personalization has been focusing on explainability and fairness. Several approaches proposed in recent works are able to explain individual recommendations…
Knowledge is Power, Understanding is Impact: Utility and Beyond Goals, Explanation Quality, and Fairness in Path Reasoning Recommendation
Giacomo Balloccu, Ludovico Boratto, Christian Cancedda +2
Path reasoning is a notable recommendation approach that models high-order user-product relations, based on a Knowledge Graph (KG). This approach can extract reasoning paths betwee…
Reinforcement Recommendation Reasoning through Knowledge Graphs for Explanation Path Quality
Giacomo Balloccu, Ludovico Boratto, Gianni Fenu +1
Numerous Knowledge Graphs (KGs) are being created to make Recommender Systems (RSs) not only intelligent but also knowledgeable. Integrating a KG in the recommendation process allo…
Regulating Group Exposure for Item Providers in Recommendation
Mirko Marras, Ludovico Boratto, Guilherme Ramos +1
Engaging all content providers, including newcomers or minority demographic groups, is crucial for online platforms to keep growing and working. Hence, while building recommendatio…
Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations
Giacomo Balloccu, Ludovico Boratto, Gianni Fenu +1
Existing explainable recommender systems have mainly modeled relationships between recommended and already experienced products, and shaped explanation types accordingly (e.g., mov…
Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations
Ludovico Boratto, Gianni Fenu, Mirko Marras +1
Enabling non-discrimination for end-users of recommender systems by introducing consumer fairness is a key problem, widely studied in both academia and industry. Current research h…
Equality of Learning Opportunity via Individual Fairness in Personalized Recommendations
Mirko Marras, Ludovico Boratto, Guilherme Ramos +1
Online educational platforms are playing a primary role in mediating the success of individuals' careers. Therefore, while building overlying content recommendation services, it be…
Interplay between Upsampling and Regularization for Provider Fairness in Recommender Systems
Ludovico Boratto, Gianni Fenu, Mirko Marras
Considering the impact of recommendations on item providers is one of the duties of multi-sided recommender systems. Item providers are key stakeholders in online platforms, and th…
Connecting User and Item Perspectives in Popularity Debiasing for Collaborative Recommendation
Ludovico Boratto, Gianni Fenu, Mirko Marras
Recommender systems learn from historical users' feedback that is often non-uniformly distributed across items. As a consequence, these systems may end up suggesting popular items…