I am Lorenzo, research scientist specialized in Recommender Systems and Information Retrieval. I am interested in understanding how Artificial Intelligence and Information Technology are impacting human life.Currently, I am working as Scientific Project Officer at the Joint Research Centre (European Commission), where I am part of the European Centre for Algorithmic Transparency (ECAT).⚠️ ⚠️ JUST ACCEPTED ⚠️ ⚠️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/10.1145/3608487Porcaro, 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).

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I hold a PhD in Information and Communication Technology (Music Technology Group, Universitat Pompeu Fabra, Spain). My PhD research has been at the intersection between Music IR and Social Computing, where the main goal has been to assess the impact of music recommendation diversity on listeners. I worked under the supervision of Prof. Emilia Gómez and Prof. Carlos Castillo. You can find my PhD dissertation at this link, and some outcomes of my research here.During the PhD, I have been part of 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 Musical AI 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.I hold a Bachelor’s degree in Applied Mathematics from “La Sapienza” University of Rome (2009-2014), a Master’s degree in Sound and Music Computing (2014-2015), and a Master’s degree in Intelligent Interactive Systems (2016-2018) from Universitat Pompeu Fabra. I also had several work experiences in the music industry as Data Engineer (SoundCloud, MonkingMe, BMAT).

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 |||
¬ 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¬ 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¬ 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).¬ 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||| 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

||| PhD Research |||
¬ Seminar Talk “Music Information Retrieval and its Application in Music Recommender Systems”. Information Retrieval Group, School of Computing Science, University of Glasgow, UK (link)¬ Seminar Talk “Diversity by Design in Music Recommender Systems”. Computer Music Group, Department of Computer Science, Institute of Mathematics and Statistics, University of São Paulo, Brazil (link)¬ Music Recommendation and Diversity: Challenges and Opportunities of the Algorithms - Micro-talk at the European Researchers' Night (EN) (link) & Workshop for the Oracle4Girls initiative (EN/ES) (link)¬ Panelist at the MUTEK Symposium 2021 “‘MUTEK Symposium: a future without gender”, panel “Promises and challenges of technology in relation to gender“ (EN/ES) (link) & MUTEK Symposium 2022 "Science and technology for a future without gender", panel "Algorithmic Justice in gender key" (ES) (link)¬ AI for Music Creation and Listening - Brochure HUMAINT  / Understanding the impact of Artificial Intelligence on human behaviour (EN) (link)¬ The Choice is Yours? How Algorithm Bias Impacts Fairness and Accessibility of Knowledge - The ORION Open Science Podcast (EN)  (link)¬ The B side of playlists: how they have changed the way they make songs -  TV3 (CAT) (link)¬ Recommendation algorithms could be widening the gender bias in music - Universitat Pompeu Fabra Website (EN/ES/CAT) (link)||| Teaching |||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)||| DivinAI |||DivinAI (Diversity in Artificial Intelligence) is an initiative of the HUMAINT project at Joint Research Center (EC) and the ICT Department at Universitat Pompeu Fabra, Barcelona.||| TROMPA |||¬ TROMPA (Towards Richer Online Music Public-domain Archives) Project, supported by the European Commission (H2020 770376) -  Use case: Music Enthusiasts¬ Blog post about RecSys 2019 participation (EN) (link)¬ Blog post about CICLing 2019 participation (EN) (link)¬ "UPF is starting a campaign to study the emotion evoked by music" - LaVanguardia (ES) (link)

||| 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)