Users in specific geographic areas often have distinct preferences regarding the provenance of the items they consume. However, current recommender systems fail to align these preferences with provider visibility, resulting in demographic inequities. By employing re-ranking, it is possible to achieve preference distribution-aware provider fairness, ensuring equitable recommendations with minimal trade-offs in effectiveness. Recommender systems …
Category: Algorithmic fairness
AMBAR: A dataset for Assessing Multiple Beyond-Accuracy Recommenders
Recommender systems are a key tool for personalization in today’s digital age. They help us discover new music, books, or movies by predicting what we might like based on past interactions. But as recommender systems evolve, researchers and practitioners recognize that traditional metrics like accuracy alone aren’t enough. Factors like fairness, diversity, and user satisfaction …
Fair Augmentation for Graph Collaborative Filtering
While fairness in Graph Collaborative Filtering remains under-explored and often inconsistent across methodologies, targeted graph augmentation can effectively mitigate demographic biases while maintaining high recommendation utility. Fairness in recommender systems is not just an ethical challenge but a measurable, achievable goal. In a paper, in collaboration with Francesco Fabbri, Gianni Fenu, Mirko Marras, and Giacomo …
Toward a Responsible Fairness Analysis: From Binary to Multiclass and Multigroup Assessment in Graph Neural Network-Based User Modeling Tasks
By transitioning from binary to multiclass and multigroup fairness metrics, hidden biases in GNN-based user modeling are uncovered. Achieving true fairness requires fine-grained evaluation of real-world data distributions to ensure equity across all user groups and attributes. In an era dominated by artificial intelligence, ensuring fairness in automated decision-making has emerged as a critical priority. …
Towards Ethical Item Ranking: A Paradigm Shift from User-Centric to Item-Centric Approaches
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 …
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, …
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 …