I am Lorenzo, research scientist specialized in Recommender Systems. I am interested in understanding how Artificial Intelligence and Information Technology are affecting people's life.Currently, I am working as Scientific Project Officer at the Joint Research Centre, where I am part of the European Centre for Algorithmic Transparency.

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<02/2024> I am honored to serve as Interaction Session chair of the next edition for the European Workshop on Algorithmic Fairness (EWAF'24).<11/2023> I will participate in the D&I panel organized within the 24th conference of the International Society for Music Information Retrieval (ISMIR).<09/2023> Porcaro, L., Vinagre, J., Frau, P., Hupont, I., & Gómez, E. (2023). Behind Recommender Systems: the Geography of the ACM RecSys Community. In 6th FAccTRec Workshop on Responsible Recommendation, co-located with the 17th ACM Conference on Recommender Systems (RecSys 2023).<07/2023> Porcaro, L., Gómez, E., & Castillo, C. (2023). Assessing the Impact of Music Recommendation Diversity on Listeners: A Longitudinal Study. ACM Transactions on Recommender Systems (July 2023 Accepted). https://doi.org/10.1145/3608487


I received my bachelor’s degree in Applied Mathematics (2014) from the Sapienza - University of Rome (Italy), and my master’s degrees in Sound and Music Computing (2015) and Intelligent Interactive Systems (2018) from Universitat Pompeu Fabra (UPF), Barcelona (Spain). From 2018 to 2022, I was involved in my PhD titled Assessing the Impact of Music Recommendation Diversity on Listeners at the Music Technology Group, part of the Department of Information and Communication Technology at the UPF, under the supervision of Prof. Emilia Gómez and Prof. Carlos Castillo. My thesis was devoted to the exploration of new methods for assessing the impact of music recommendation diversity on listeners’ behaviours and attitudes, and I provided empirical evidence of the function that diversity plays in mediating the relationships between music recommendations and listeners. I received the doctor diploma from the UPF with maximum qualification ("cum laude” ) in 2022.During the PhD, I have been part of the TROMPA Project (Towards Richer Online Music Public-domain Archives), an international research project sponsored by the European Union, investigating how to make public-domain digital music resources more accessible. I have also collaborated with the MusicalAI project, funded by the Ministry of Science and Innovation of the Spanish Government, investigating AI to support musical experiences towards a data-driven, human-centred approach. Before starting my PhD, I also had several work experiences in the music industry covering data-engineering roles. In 2015, I did an internship at SoundCloud, one of the largest music streaming services in the world. Afterwards, I started working at MonkingMe, a Catalan startup building a local music streaming platform, and then at BMAT, a company that monitors and reports music usage globally across TVs, radios, venues and digital platforms.Since November 2022, I have been working as a Scientific Project Officer at the European Commission’s Joint Research Centre (JRC), where I joined the Human Behaviour and Machine Intelligence (HUMAINT) team, part of the Algorithmic Transparency Unit. In this role, I am conducting research on the trustworthiness of recommender systems in the context of the recently enforced Digital Service Act (DSA). I am also part of the European Centre for Algorithmic Transparency (ECAT). My current research focuses on exploring new methods for auditing recommender systems, and in parallel I am also collaborating on a multi-disciplinary project on the impact of social media on adolescents’ mental health.


You can find most of the preprints of the articles below in arXiv or in ResearchGate.


||| 2023 |||
¬ Porcaro, L., Vinagre, J., Frau, P., Hupont, I., & Gómez, E. (2023). Behind Recommender Systems: the Geography of the ACM RecSys Community. In 6th FAccTRec Workshop on Responsible Recommendation, co-located with the 17th ACM Conference on Recommender Systems (RecSys 2023).
¬ 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¬ Porcaro, L., Castillo, C., Gómez, E., & Vinagre, J. (2023). Fairness and Diversity in Information Access Systems. In EWAF23: European Workshop on Algorithmic Fairness, June 7-9.Hupont, I., Tolan, S., Frau, P., Porcaro, L., & Gomez, E. (2023). Measuring and fostering diversity in Affective Computing research. IEEE Transactions on Affective Computing, PP(8), 1–16. https://doi.org/10.1109/TAFFC.2023.3244041||| 2022 |||
¬ Porcaro, L., Gómez, E., & Castillo, C. (2022). Diversity in the Music Listening Experience: Insights from Focus Group Interviews. In CHIIR ’22: ACM SIGIR Conference on Human Information Interaction and Retrieval, March 14–18. https://doi.org/10.1145/3498366.3505778
¬ 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¬ 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¬ Gómez-Cañón, J. S., Gutiérrez-Páez, N., Porcaro, L., Porter, A., Cano, E., Herrera-Boyer, P., Gkiokas, A., Santos, P., Hernández-Leo, D., Karreman, C., & Gómez, E. (2022). TROMPA-MER: an open dataset for personalized music emotion recognition. Journal of Intelligent Information Systems, 0123456789. https://doi.org/10.1007/s10844-022-00746-0¬ Hupont, I., Gómez, E., Tolan, S., Porcaro, L., & Freire, A. (2022). Monitoring Diversity of AI Conferences: Lessons Learnt and Future Challenges in the DivinAI Project. AAAI 2022 Workshop on Artificial Intelligence Diversity, Belonging, Equity, and Inclusion (AIDBEI).||| 2021 |||
¬ 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).¬ Hupont, I., Tolan, S., Freire, A., Porcaro, L., Estevez, S., & Gómez, E. (2021). How diverse is the ACII community? Analysing gender, geographical and business diversity of Affective Computing research. International Conference on Affective Computing & Intelligent Interaction (ACII 2021). http://doi.org/10.1109/ACII52823.2021.9597426¬ Gómez-Cañón, J.S., Gutiérrez-Páez, N., Porcaro, L., Gkiokas, A., Herrera,P., Gómez, E. (2021). Improving emotion annotation of music using citizen science. 16th International Conference on Music Perception and Cognition (ICMPC-ESCOM2021)¬ Gutiérrez Páez, N.F., Gómez-Cañón, J.S., Porcaro, L., Santos, P., Hernández-Leo, D., Gómez, E. (2021). Emotion Annotation of Music: A Citizen Science Approach. In: Hernández-Leo, D., Hishiyama, R., Zurita, G., Weyers, B., Nolte, A., Ogata, H. (eds) Collaboration Technologies and Social Computing. CollabTech 2021. Lecture Notes in Computer Science, vol 12856. Springer, Cham. https://doi.org/10.1007/978-3-030-85071-5_4||| 2020 |||
¬ Shakespeare, D., Porcaro, L., Gómez, E., and Castillo, C. . (2020) Exploring Artist Gender Bias in Music Recommendation. 2nd Workshop on the Impact of Recommender Systems (ImpactRS), co-located with the 14th ACM Conference on Recommender Systems (RecSys 2020). Virtual, 22nd-26th September.
¬ Ferraro, A., Porcaro, L., Serra, X. (2020). Balancing Exposure and Relevance in Academic Search. NIST Conference 2020.||| 2019 |||
¬ Porcaro, L., Gómez, E. (2019). 20 Years of Playlists: A Statistical Analysis on Popularity and Diversity. 20th Conference of the International Society for Music Information Retrieval (ISMIR 2019). TU Delft, Delft, 4th-8th November.
¬ Porcaro, L., Castillo, C., Gómez, E. (2019). Music Recommendation Diversity: A Tentative Framework and Preliminary Results. 1st Workshop on Designing Human-Centric MIR Systems (wsHCMIR19), co-located with the 20th Conference of the International Society for Music Information Retrieval (ISMIR 2019). TU Delft, Delft, 4th-8th November.¬ Porcaro, L., Gómez, E.. (2019). A Model for Evaluating Popularity and Semantic Information Variations in Radio Listening Sessions. 1st Workshop on the Impact of Recommender Systems (ImpactRS), co-located with the 13th ACM Conference on Recommender Systems (RecSys 2019). Copenhagen, 16th-20th September. https://ceur-ws.org/Vol-2462¬ Porcaro, L., Saggion, H. (2019). Recognizing Musical Entities in User-generated Content. Computación y Sistemas, 23(3). Presented at International Conference on Computational Linguistics and Intelligent Text Processing (CICLing) 2019, University of La Rochelle, La Rochelle, 7th-13th April. https://doi.org/10.13053/cys-23-3-3280||| <2019 |||
¬ Porcaro, L., (2018). Information Extraction from User-generated Content in the Classical Music Domain. Master thesis, Universitat Pompeu Fabra, Barcelona, Spain.
¬ Porcaro, L., (2015). Modeling Lemur vocalizations from a signal processing perspective. Master thesis, Universitat Pompeu Fabra, Barcelona, Spain. https://doi.org/10.5281/zenodo.3726948


06/11/2023: Panellist at 24th International Society for Music Information Retrieval Conference, “D&I Panel”, Milan, IT, (link).24/10/2023: Invited seminar talk: “Assessing the Impact of Music Recommendation Diversity on Listeners”. BBC, online.04/07/2023: Invited seminar talk: “Music Information Retrieval and its Application in Music Recommender Systems”. Italian National PhD Program in AI, University of Pisa, online.06/02/2023: Invited seminar talk: “Music Information Retrieval and its Application in Music Recommender Systems”. Information Retrieval Group, University of Glasgow, online, (link).04/12/2022: Tutorial: “Trustworthy MIR: Creating MIR applications with values”, at the 23rd International Society for Music Information Retrieval Conference, online, (link).29/03/2022: Invited seminar talk: “Diversity by Design in Music Recommender Systems”. Computer Music Group, Department of Computer Science, University of São Paulo, online, (link).03/03/2022: Panellist at the MUTEK International Festival of Digital Creativity 2022: “MUTEK Symposium: Science and technology for a future without gender - Algorithmic Justice in Gender key”, Barcelona (Spain), (link).12/02/2022: Workshop for the Oracle4Girls initiative: “Music Recommendation and Diversity: Challenges and Opportunities of the Algorithms”, online, (link).23/09/2021: Micro-talk at the European Researchers' Night: “Music Recommendation and Diversity: Challenges and Opportunities of the Algorithms”, Barcelona (Spain), (link).21/04/2021: Panellist at the MUTEK International Festival of Digital Creativity 2021: “'MUTEK Symposium: a future without gender - Promises and challenges of technology in relation to gender”, Barcelona (Spain), (link).8/03/2021: Lesson on "Music IR Evaluation Practices" prepared for the Music Information Retrieval course of the Master in Sound and Music Computing, organized by the Music Technology Group (Universitat Pompeu Fabra) (link).22/01/2021: Brochure HUMAINT - Understanding the impact of Artificial Intelligence on human behaviour, AI for Music Creation and Listening, (link).16/03/2021: Invited seminar talk: “Perceptions of Diversity in Electronic Music: The Impact of Listener, Artist, and Track Characteristics”, Spotify, online.16/11/2020: Interview for the ORION Open Science Podcast: “The Choice is Yours? How Algorithm Bias Impacts Fairness and Accessibility of Knowledge”, online, (link).21/10/2020: Interview for TV3 (Catalan public broadcaster): “The B side of playlists: how they have changed the way they make songs” (“La cara B de les playlists: com han canviat la forma de fer cançons”), online, (link).14/10/2020: News article on Universitat Pompeu Fabra website, "Recommendation algorithms could be widening the gender bias in music", (link).


||| 2023 |||
¬ Porcaro, L., Vinagre, J., Frau, P., Hupont, I., & Gómez, E. (2023). Behind Recommender Systems: the Geography of the ACM RecSys Community. In 6th FAccTRec Workshop on Responsible Recommendation, co-located with the 17th ACM Conference on Recommender Systems (RecSys 2023), (video)
||| 2022 |||
¬ Porcaro, L. (2022). Assessing the impact of music recommendation diversity on listeners. PhD dissertation. Music Technology Group, Universitat Pompeu Fabra, Spain. (slides, video)
¬ Bauer, C., Ferraro, A., Gómez, E., Porcaro, L. (2022). Trustworthy MIR: Creating MIR applications with values. Tutorial at 23th Conference of the International Society for Music Information Retrieval (ISMIR 2022) (slides, video)¬ 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, Seul, Korea (slides, video)¬ Porcaro, L., Gómez, E., & Castillo, C. (2022). Diversity in the Music Listening Experience: Insights from Focus Group Interviews. In CHIIR ’22: ACM SIGIR Conference on Human Information Interaction and Retrieval, March 14–18, 2022, Regensburg, Bavaria. (poster, video)¬ 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. (slides, video)||| 2021 |||
¬ Porcaro, L., Castillo, C., Gómez, E. (2021). Assessing the Impact of Music Recommendation Diversity. The advanced course on AI (ACAI2021) on Human Centered AI. European Association for Artificial Intelligence (EurAI), Berlin International University of Applied Sciences, Berlin, Germany. (poster)
||| 2020 |||
¬ Shakespeare, D., Porcaro, L., Gómez, E., and Castillo, C. . (2020). Exploring Artist Gender Bias in Music Recommendation. 2nd Workshop on the Impact of Recommender Systems (ImpactRS), co-located with the 14th ACM Conference on Recommender Systems (RecSys 2020). Virtual, 22nd-26th September (slides, video)
||| 2019 |||
¬ Porcaro, L., Gómez, E. (2019). 20 Years of Playlists: A Statistical Analysis on Popularity and Diversity. 20th Conference of the International Society for Music Information Retrieval (ISMIR 2019). TU Delft, Delft, 4th-8th November (slides, poster, video)
¬ Porcaro, L., Castillo, C., Gómez, E. (2019). Music Recommendation Diversity: A Tentative Framework and Preliminary Results. 1st Workshop on Designing Human-Centric MIR Systems (wsHCMIR19), co-located with the 20th Conference of the International Society for Music Information Retrieval (ISMIR 2019). TU Delft, Delft, 4th-8th November. (slides)¬ Porcaro, L., Gómez, E.. (2019). A Model for Evaluating Popularity and Semantic Information Variations in Radio Listening Sessions. 1st Workshop on the Impact of Recommender Systems (ImpactRS), co-located with the 13th ACM Conference on Recommender Systems (RecSys 2019). Copenhagen, 16th-20th September (slides)¬ Porcaro, L., Saggion, H. (2019). Recognizing Musical Entities in User-generated Content. International Conference on Computational Linguistics and Intelligent Text Processing (CICLing) 2019, University of La Rochelle, La Rochelle, 7th-13th April (slides, poster)