Francesco Fabbri

About Me

Hi! Welcome to my personal homepage.

I am Francesco Fabbri, Research Scientist at Spotify, where I work on Representation Learning and Generative AI for personalization. I earned my Ph.D. in Computer Science (cum laude) from Pompeu Fabra University, Barcelona, focusing on analyzing and mitigating unintended effects in online social platforms. During my Ph.D., I was a visiting researcher at the University of Helsinki, working on responsible recommender systems. I also interned at Huawei, where I worked on Federated Learning for personalization.

My research has been published in top-tier conferences including CIKM, theWebConf, ICWSM, and ECIR. I also won the Best Paper Award at theWebConf and the Ted Nelson Award at Hypertext. Before pursuing my Ph.D., I obtained a Master’s Degree (Honours) in Data Science and a Bachelor of Science in Statistics both from Sapienza, University of Rome.

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Contact

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Publications

  1. Diffusion Model for Slate Recommendation F. Tomasi, F. Fabbri, M. Lalmas, Z. Dai, arXiv [link] (2024)
  2. Robustness in Fairness Against Edge-Level Perturbations in GNN-Based Recommendation L. Boratto, F. Fabbri, G. Fenu, M. Marras, G. Medda Advances in Information Retrieval - 46th European Conference on Information Retrieval, (ECIR) 2024, Glasgow, UK, March 24-28, 2024, Proceedings, Part (III) [link] (2024)
  3. Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks M. Nadai, F. Fabbri, P. Gigioli, A. Wang, A. Li, F. Silvestri, L. Kim, S. Lin, V. Radosavljevic, S. Ghael, D. Nyhan, H. Bouchard, M. Lalmas, A. Damianou Companion Proceedings of the (ACM) on Web Conference 2024, (WWW) 2024, Singapore, Singapore, May 13-17, 2024 [link] (2024)
  4. Towards Graph Foundation Models for Personalization A. Damianou, F. Fabbri, P. Gigioli, M. Nadai, A. Wang, E. Palumbo, M. Lalmas Companion Proceedings of the (ACM) on Web Conference 2024, (WWW) 2024, Singapore, Singapore, May 13-17, 2024 [link] (2024)
  5. Fair Max-Min Diversity Maximization in Streaming and Sliding-Window Models Y. Wang, F. Fabbri, M. Mathioudakis, J. Li Entropy [link] (2023)
  6. Counterfactual Graph Augmentation for Consumer Unfairness Mitigation in Recommender Systems L. Boratto, F. Fabbri, G. Fenu, M. Marras, G. Medda Proceedings of the 32nd (ACM) International Conference on Information and Knowledge Management, (CIKM) 2023, Birmingham, United Kingdom, October 21-25, 2023 [link] (2023)
  7. Graph Learning for Exploratory Query Suggestions in an Instant Search System E. Palumbo, A. Damianou, A. Wang, A. Liu, G. Fazelnia, F. Fabbri, R. Ferreira, F. Silvestri, H. Bouchard, C. Hauff, M. Lalmas, B. Carterette, P. Chandar, D. Nyhan Proceedings of the 32nd (ACM) International Conference on Information and Knowledge Management, (CIKM) 2023, Birmingham, United Kingdom, October 21-25, 2023 [link] (2023)
  8. The Interconnected Nature of Online Harm and Moderation: Investigating the Cross-Platform Spread of Harmful Content between YouTube and Twitter V. Gatta, L. Luceri, F. Fabbri, E. Ferrara Proceedings of the 34th (ACM) Conference on Hypertext and Social Media, (HT) 2023, Rome, Italy, September 4-8, 2023 [link] (2023)
  9. Rewiring What-to-Watch-Next Recommendations to Reduce Radicalization Pathways (Extended Abstract) F. Fabbri, Y. Wang, F. Bonchi, C. Castillo, M. Mathioudakis Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, (IJCAI) 2023, 19th-25th August 2023, Macao, SAR, China [link] (2023)
  10. Max-Min Diversification with Fairness Constraints: Exact and Approximation Algorithms Y. Wang, M. Mathioudakis, J. Li, F. Fabbri Proceedings of the 2023 (SIAM) International Conference on Data Mining, (SDM) 2023, Minneapolis-St. Paul Twin Cities, MN, USA, April 27-29, 2023 [link] (2023)
  11. GNNUERS: Fairness Explanation in GNNs for Recommendation via Counterfactual Reasoning G. Medda, F. Fabbri, M. Marras, L. Boratto, G. Fenu Preprint: arxiv:2304.06182 [link] (2023)
  12. Algorithmic bias in graph-based recommender systems F. Fabbri PhD Thesis [link] (2022)
  13. Streaming Algorithms for Diversity Maximization with Fairness Constraints Y. Wang, F. Fabbri, M. Mathioudakis 38th (IEEE) International Conference on Data Engineering, (ICDE) 2022, Kuala Lumpur, Malaysia, May 9-12, 2022 [link] (2022)
  14. Exposure Inequality in People Recommender Systems: The Long-Term Effects F. Fabbri, M. Croci, F. Bonchi, C. Castillo Proceedings of the Sixteenth International (AAAI) Conference on Web and Social Media, (ICWSM) 2022, Atlanta, Georgia, USA, June 6-9, 2022 [link] (2022)
  15. Rewiring What-to-Watch-Next Recommendations to Reduce Radicalization Pathways F. Fabbri, Y. Wang, F. Bonchi, C. Castillo, M. Mathioudakis (WWW) ‘22: The (ACM) Web Conference 2022, Virtual Event, Lyon, France, April 25 - 29, 2022 [link] (2022)
  16. Comparing Equity and Effectiveness of Different Algorithms in an Application for the Room Rental Market D. Solans, F. Fabbri, C. Calsamiglia, C. Castillo, F. Bonchi (AIES) ‘21: (AAAI/ACM) Conference on AI, Ethics, and Society, Virtual Event, USA, May 19-21, 2021 [link] (2021)
  17. From the Beatles to Billie Eilish: Connecting Provider Representativeness and Exposure in Session-Based Recommender Systems A. Ariza, F. Fabbri, L. Boratto, M. Salam Advances in Information Retrieval - 43rd European Conference on (IR) Research, (ECIR) 2021, Virtual Event, March 28 - April 1, 2021, Proceedings, Part (II) [link] (2021)
  18. Fair and Representative Subset Selection from Data Streams Y. Wang, F. Fabbri, M. Mathioudakis (WWW) ‘21: The Web Conference 2021, Virtual Event / Ljubljana, Slovenia, April 19-23, 2021 [link] (2021)
  19. The Effect of Homophily on Disparate Visibility of Minorities in People Recommender Systems F. Fabbri, F. Bonchi, L. Boratto, C. Castillo Proceedings of the Fourteenth International (AAAI) Conference on Web and Social Media, (ICWSM) 2020, Held Virtually, Original Venue: Atlanta, Georgia, USA, June 8-11, 2020 [link] (2020)