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: Recommender systems
KGGLM: A Generative Language Model for Generalizable Knowledge Graph Representation Learning in Recommendation
Current recommender systems struggle to unify knowledge representation across tasks, leading to inefficiencies and reduced interpretability. KGGLM addresses this by leveraging generative language models for generalizable and task-adaptive knowledge graph learning, achieving state-of-the-art performance in both knowledge completion and recommendation. Recommender systems are central in personalizing user experiences across domains, from e-commerce to entertainment. A …
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 …
SM-RS: Single- and Multi-Objective Recommendations with Contextual Impressions and Beyond-Accuracy Propensity Scores
A recommender system is only as effective as its understanding of user propensities. The SM-RS dataset links contextual impressions with self-reported preferences, enabling the development of personalized, multi-objective recommendations. Recommender systems (RS) have long focused on delivering accurate results, aiming to align recommendations with user profiles. However, as user expectations evolve, beyond-accuracy metrics such as …
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, …