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 …
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 …
Rows or Columns? Minimizing Presentation Bias When Comparing Multiple Recommender Systems
Under presentation bias, the attention of the users to the items in a recommendation list changes, thus affecting their possibility to be considered and the effectiveness of a model. When comparing different layouts through which recommendations are presented, presentation bias impacts users clicking behavior (low-level feedback), but not so much the perceived performance of a …
FairUP: A Framework for Fairness Analysis of Graph Neural Network-Based User Profiling Models
Modern user profiling approaches capture different forms of interactions with the data, from user-item to user-user relationships. Graph Neural Networks (GNNs) have become a natural way to model these behaviors and build efficient and effective user profiles. However, each GNN-based user profiling approach has its own way of processing information, thus creating heterogeneity that does …