By eliminating user-centric biases and adopting a purely item-focused approach, it is possible to achieve ethical and effective ranking systems—ensuring fairness, resilience, and compliance with regulations on responsible AI. Ranking systems are essential in online platforms, shaping user experiences and influencing product visibility and sales. However, traditional user-centric ranking systems, which assign reputation scores to …
Unmasking Privacy: A Reproduction and Evaluation Study of Obfuscation-based Perturbation Techniques for Collaborative Filtering
A well-designed obfuscation framework can significantly enhance user privacy in recommender systems without fundamentally compromising their performance, offering a viable path to balancing personalization and privacy. As digital platforms increasingly rely on personalization to engage users, recommender systems have become a central component of e-commerce and entertainment industries. However, this personalization often comes at the …
Bringing Equity to Coarse and Fine-Grained Provider Groups in Recommender Systems
Achieving true fairness in recommender systems requires moving beyond broad demographic categories to address disparities at a fine-grained level, ensuring equitable representation for all subgroups. This goal can be made feasible through advanced re-ranking methodologies like CONFIGRE. Recommender systems are ubiquitous in today’s digital landscape, providing tailored suggestions to users in domains like e-commerce, entertainment, …
User Perceptions of Diversity in Recommender Systems
Understanding user perceptions of diversity in recommender systems reveals a paradox: while users favor intuitive, metadata-driven metrics like genres, their ability to distinguish finer variations in diversity is limited, highlighting the need for user-aligned algorithms that balance diversity with relevance. In this study, in collaboration with Patrik Dokoupil and Ladislav Peska, and published in the …
GNNUERS: Unfairness Explanation in Recommender Systems through Counterfactually-Perturbed Graphs
Counterfactual reasoning can be effectively employed to perturb user-item interactions, to identify and explain unfairness in GNN-based recommender systems, thus paving the way for more equitable and transparent recommendations. In this study, in collaboration with Francesco Fabbri, Gianni Fenu, Mirko Marras, and Giacomo Medda, and published in the ACM Transactions on Intelligent Systems and Technology, …
Robustness in Fairness against Edge-level Perturbations in GNN-based Recommendation
Edge-level perturbations impact the robustness and fairness of graph-based recommender systems, revealing significant vulnerabilities and the need for more resilient design approaches. In our paper, which will be presented at the ECIR 2024 conference, we delve into the robustness of graph-based recommendation systems against edge-level perturbations. This work is a collaborative effort with Francesco Fabbri, …
A Cost-Sensitive Meta-Learning Strategy for Fair Provider Exposure in Recommendation
Cost-sensitive meta-learning can effectively balance exposure fairness in recommendation systems without compromising their utility. In our paper, which will be presented at the ECIR 2024 conference, we introduce a novel cost-sensitive meta-learning technique aimed at enhancing fairness in recommendation systems. Our work addresses a critical issue in many online platforms – ensuring equitable exposure for …
MOReGIn: Multi-Objective Recommendation at the Global and Individual Levels
It is possible to provide effective recommendations while simultaneously optimizing beyond-accuracy perspectives for the individual users (e.g., genre calibration) and, globally, for the entire system (e.g., provider fairness). In a study, with Elizabeth Gómez, David Contreras, and Maria Salamó, published in the proceedings of ECIR 2024, we present a model designed to meet both global …
Counterfactual Graph Augmentation for Consumer Unfairness Mitigation in Recommender Systems
It is possible to effectively address consumer unfairness in recommender systems by using counterfactual explanations to augment the user-item interaction graph. This approach not only leads to fairer outcomes across different demographic groups but also maintains or improves the overall utility of the recommendations. In a study with Francesco Fabbri, Gianni Fenu, Mirko Marras, and …
Reproducibility of Multi-Objective Reinforcement Learning Recommendation: Interplay between Effectiveness and Beyond-Accuracy Perspectives
Controlling various objectives within Multi-Objective Recommender Systems (MORSs). While reinforcing accuracy objectives appears feasible, it is more challenging to individually control diversity and novelty due to their positive correlation. This raises critical questions about the effectiveness of incorporating multiple correlated objectives in MORSs and the potential risks of not having control over them. In a …