Lorenzo Porcaro is a research scientist specializing in Recommender Systems and Human-Computer Interaction. He earned a bachelor's degree in Applied Mathematics from Sapienza - University of Rome (Italy) in 2014, and master's degrees in Sound and Music Computing (2015) and Intelligent Interactive Systems (2018) from Universitat Pompeu Fabra (UPF), Barcelona (Spain). He completed his PhD titled “Assessing the Impact of Music Recommendation Diversity on Listeners” at the Music Technology Group (UPF) in 2022. Since November 2022, he has been working as a Scientific Project Officer at the European Commission’s Joint Research Centre (JRC) in the Human Behaviour and Machine Intelligence (HUMAINT) team, focusing on recommender systems evaluation.
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Selected publications:
Porcaro, L., Gómez, E., & Castillo, C. (2023). Assessing the Impact of Music Recommendation Diversity on Listeners: A Longitudinal Study. ACM Transactions on Recommender Systems (Just Accepted). https://doi.org/10.1145/3608487
Patro, G.K., Porcaro, L., Mitchell, L., Zhang, Q., Zehlike, M., Garg, N. (2022). Fair ranking: a critical review, challenges, and future directions. In FAccT ’22: ACM Conference on Fairness, Accountability, and Transparency, June 21–24. https://doi.org/10.1145/3531146.3533238
Porcaro, L., Gómez, E., & Castillo, C. (2022). Perceptions of Diversity in Electronic Music: the Impact of Listener, Artist, and Track Characteristics. Proc. ACM Hum.-Comput. Interact. 6, CSCW1, Article 109 (April 2022), 26 pages. https://doi.org/10.1145/3512956
Porcaro, L., Castillo, C. & Gómez, E. (2021). Diversity by Design in Music Recommender Systems. Transactions of the International Society for Music Information Retrieval (TISMIR). http://doi.org/10.5334/tismir.106
Freire, A., Porcaro, L., and Gómez, E. (2021). Measuring Diversity of Artificial Intelligence Conferences. AAAI 2021 Workshop on Artificial Intelligence Diversity, Belonging, Equity, and Inclusion (AIDBEI).