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 …
Robust Privacy-Preserving Federated Item Ranking in Online Marketplaces: Exploiting Platform Reputation for Effective Aggregation
Centralized item ranking in online marketplaces compromises user privacy and is vulnerable to manipulation. The introduction of a federated, reputation-based ranking system preserves privacy, ensures fairness, and delivers robust and effective rankings. The growth of online marketplaces has transformed consumer experiences, offering diverse products aggregated from multiple sellers. However, the centralized nature of these platforms …
EDGE: A Conversational Interface driven by Large Language Models for Educational Knowledge Graphs Exploration
Navigating educational data is a growing challenge. EDGE offers a fusion of large language models and knowledge graphs to enable intuitive, natural language-driven exploration, empowering educators, learners, and administrators with actionable insights for accessing and understanding educational ecosystems. In an era where digital education platforms generate vast amounts of data, navigating and making sense of …
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 …
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. …
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 …
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 …
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 …