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, …
Tag: Recommender systems
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 …
Towards Self-Explaining Sequence-Aware Recommendation
The sequence of user-item interactions can be effectively incorporated in the generation of personalized explanations in recommender systems. By modeling user behavior history sequentially, it is possible to enhance the quality and personalization of explanations provided alongside recommendations, without affecting recommendation quality. In a study with Alejandro Ariza-Casabona, Maria Salamó, and Gianni Fenu, published in …
Looks Can Be Deceiving: Linking User-Item Interactions and User’s Propensity Towards Multi-Objective Recommendations
Users’ claimed willingness to interact with novel and diverse items doesn’t always match the recommendations they accept. While users may express a desire for novelty and diversity in recommendations, their actual choices often gravitate towards relevance. This key finding challenges the conventional approach in multi-objective recommender system design, emphasizing the necessity of aligning system objectives …
Disparate Impact in Item Recommendation: a Case of Geographic Imbalance
Data imbalances, related to the country of production of an item, lead to the under-recommendation of items produced in the smaller (less represented) countries. Re-ranking the recommendation lists, by balancing item relevance with the promotion of items produced in smaller countries can introduce equity in terms of visibility and exposure, without affecting recommendation effectiveness. In …
The effect of homophily on disparate visibility of minorities in people recommender systems
Demographics and homophily are the main drivers behind people recommendation in social networks and can affect the visibility that is given to users when they are recommended. These phenomena mainly impact users who belong to the minority groups, which have lower possibilities of being recommended, unless they are highly homophilic. In a recent ICWSM 2020 …