136 citations · 356 across the 23 of their papers we have counts for
23 papers
A Reproducible and Fair Evaluation of Partition-aware Collaborative Filtering
Domenico de Gioia, Claudio Pomo, Ludovico Boratto +1
Similarity-based collaborative filtering (CF) models have long demonstrated strong offline performance and conceptual simplicity. However, their scalability is limited by the quadr…
How Fair is Your Diffusion Recommender Model?
Daniele Malitesta, Giacomo Medda, Erasmo Purificato +3
Diffusion-based learning has settled as a rising paradigm in generative recommendation, outperforming traditional approaches built upon variational autoencoders and generative adve…
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…
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…
Looks Can Be Deceiving: Linking User-Item Interactions and User's Propensity Towards Multi-Objective Recommendations
Patrik Dokoupil, Ladislav Peska, Ludovico Boratto
Multi-objective recommender systems (MORS) provide suggestions to users according to multiple (and possibly conflicting) goals. When a system optimizes its results at the individua…
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…
Robust Reputation Independence in Ranking Systems for Multiple Sensitive Attributes
Guilherme Ramos, Ludovico Boratto, Mirko Marras
Ranking systems have an unprecedented influence on how and what information people access, and their impact on our society is being analyzed from different perspectives, such as us…
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…
What's Your Value of Travel Time? Collecting Traveler-Centered Mobility Data via Crowdsourcing
Cristian Consonni, Silvia Basile, Matteo Manca +5
Mobility and transport, by their nature, involve crowds and require the coordination of multiple stakeholders - such as policy-makers, planners, transport operators, and the travel…
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…
ECIR 2020 Workshops: Assessing the Impact of Going Online
Sérgio Nunes, Suzanne Little, Sumit Bhatia +11
ECIR 2020 https://ecir2020.org/ was one of the many conferences affected by the COVID-19 pandemic. The Conference Chairs decided to keep the initially planned dates (April 14-17, 2…
A Robust Reputation-based Group Ranking System and its Resistance to Bribery
Joao Saude, Guilherme Ramos, Ludovico Boratto +1
The spread of online reviews and opinions and its growing influence on people's behavior and decisions, boosted the interest to extract meaningful information from this data deluge…