51 citations · 168 across the 24 of their papers we have counts for
24 papers
Deploying Semantic ID-based Generative Retrieval for Large-Scale Podcast Discovery at Spotify
Edoardo D'Amico, Marco De Nadai, Praveen Chandar +41
Podcast listening is often grounded in a set of favorite shows, while listener intent can evolve over time. This combination of stable preferences and changing intent motivates rec…
From IR to RecSys: Evaluating LLM-based Judges in Cranfield-style Recommendation Collections
Gustavo Penha, Aleksandr V. Petrov, Claudia Hauff +9
The Cranfield paradigm has long provided reliable, reproducible evaluation in ad hoc retrieval, and recent work has begun extending this framework to recommender systems. A recent…
Semantic IDs for Joint Generative Search and Recommendation
Gustavo Penha, Edoardo D'Amico, Marco De Nadai +8
Generative models powered by Large Language Models (LLMs) are emerging as a unified solution for powering both recommendation and search tasks. A key design choice in these models…
Evaluating Podcast Recommendations with Profile-Aware LLM-as-a-Judge
Francesco Fabbri, Gustavo Penha, Edoardo D'Amico +7
Evaluating personalized recommendations remains a central challenge, especially in long-form audio domains like podcasts, where traditional offline metrics suffer from exposure bia…
Policy-as-Prompt: Rethinking Content Moderation in the Age of Large Language Models
Konstantina Palla, José Luis Redondo García, Claudia Hauff +5
Content moderation plays a critical role in shaping safe and inclusive online environments, balancing platform standards, user expectations, and regulatory frameworks. Traditionall…
IOHunter: Graph Foundation Model to Uncover Online Information Operations
Marco Minici, Luca Luceri, Francesco Fabbri +1
Social media platforms have become vital spaces for public discourse, serving as modern agoràs where a wide range of voices influence societal narratives. However, their open natur…
Algorithmic Drift: A Simulation Framework to Study the Effects of Recommender Systems on User Preferences
Erica Coppolillo, Simone Mungari, Ettore Ritacco +4
Digital platforms such as social media and e-commerce websites adopt Recommender Systems to provide value to the user. However, the social consequences deriving from their adoption…
Fair Augmentation for Graph Collaborative Filtering
Ludovico Boratto, Francesco Fabbri, Gianni Fenu +2
Recent developments in recommendation have harnessed the collaborative power of graph neural networks (GNNs) in learning users' preferences from user-item networks. Despite emergin…
Prompt-to-Slate: Diffusion Models for Prompt-Conditioned Slate Generation
Federico Tomasi, Francesco Fabbri, Justin Carter +3
Slate generation is a common task in streaming and e-commerce platforms, where multiple items are presented together as a list or ``slate''. Traditional systems focus mostly on ite…
Absolute and Relative Ambiguity Attitudes
Francesco Fabbri, Giulio Principi, Lorenzo Stanca
We represent preferences that exhibit absolute or relative attitudes towards ambiguity without assuming convexity of preferences. Our analysis is motivated by the recent experiment…
Towards Graph Foundation Models for Personalization
Andreas Damianou, Francesco Fabbri, Paul Gigioli +4
In the realm of personalization, integrating diverse information sources such as consumption signals and content-based representations is becoming increasingly critical to build st…
Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks
Marco De Nadai, Francesco Fabbri, Paul Gigioli +11
In the ever-evolving digital audio landscape, Spotify, well-known for its music and talk content, has recently introduced audiobooks to its vast user base. While promising, this mo…
Robustness in Fairness against Edge-level Perturbations in GNN-based Recommendation
Ludovico Boratto, Francesco Fabbri, Gianni Fenu +2
Efforts in the recommendation community are shifting from the sole emphasis on utility to considering beyond-utility factors, such as fairness and robustness. Robustness of recomme…
FedFNN: Faster Training Convergence Through Update Predictions in Federated Recommender Systems
Francesco Fabbri, Xianghang Liu, Jack R. McKenzie +2
Federated Learning (FL) has emerged as a key approach for distributed machine learning, enhancing online personalization while ensuring user data privacy. Instead of sending privat…
Counterfactual Graph Augmentation for Consumer Unfairness Mitigation in Recommender Systems
Ludovico Boratto, Francesco Fabbri, Gianni Fenu +2
In recommendation literature, explainability and fairness are becoming two prominent perspectives to consider. However, prior works have mostly addressed them separately, for insta…
The Interconnected Nature of Online Harm and Moderation: Investigating the Cross-Platform Spread of Harmful Content between YouTube and Twitter
Valerio La Gatta, Luca Luceri, Francesco Fabbri +1
The proliferation of harmful content shared online poses a threat to online information integrity and the integrity of discussion across platforms. Despite various moderation inter…
GNNUERS: Fairness Explanation in GNNs for Recommendation via Counterfactual Reasoning
Giacomo Medda, Francesco Fabbri, Mirko Marras +2
Nowadays, research into personalization has been focusing on explainability and fairness. Several approaches proposed in recent works are able to explain individual recommendations…
Max-Min Diversification with Fairness Constraints: Exact and Approximation Algorithms
Yanhao Wang, Michael Mathioudakis, Jia Li +1
Diversity maximization aims to select a diverse and representative subset of items from a large dataset. It is a fundamental optimization task that finds applications in data summa…
Streaming Algorithms for Diversity Maximization with Fairness Constraints
Yanhao Wang, Francesco Fabbri, Michael Mathioudakis
Diversity maximization is a fundamental problem with wide applications in data summarization, web search, and recommender systems. Given a set of elements, it asks to selec…
Rewiring What-to-Watch-Next Recommendations to Reduce Radicalization Pathways
Francesco Fabbri, Yanhao Wang, Francesco Bonchi +2
Recommender systems typically suggest to users content similar to what they consumed in the past. If a user happens to be exposed to strongly polarized content, she might subsequen…
Exposure Inequality in People Recommender Systems: The Long-Term Effects
Francesco Fabbri, Maria Luisa Croci, Francesco Bonchi +1
People recommender systems may affect the exposure that users receive in social networking platforms, influencing attention dynamics and potentially strengthening pre-existing ineq…
Fair and Representative Subset Selection from Data Streams
Yanhao Wang, Francesco Fabbri, Michael Mathioudakis
We study the problem of extracting a small subset of representative items from a large data stream. In many data mining and machine learning applications such as social network ana…
The relationship between human mobility and viral transmissibility during the COVID-19 epidemics in Italy
Paolo Cintia, Luca Pappalardo, Salvatore Rinzivillo +29
In 2020, countries affected by the COVID-19 pandemic implemented various non-pharmaceutical interventions to contrast the spread of the virus and its impact on their healthcare sys…
Mobile phone data analytics against the COVID-19 epidemics in Italy: flow diversity and local job markets during the national lockdown
Pietro Bonato, Paolo Cintia, Francesco Fabbri +12
Understanding collective mobility patterns is crucial to plan the restart of production and economic activities, which are currently put in stand-by to fight the diffusion of the e…