Browsing by Author "Porcaro, Lorenzo"

Sort by: Order: Results:

  • 20 years of playlists: A statistical analysis on popularity and diversity
    Porcaro, Lorenzo; Gómez Gutiérrez, Emilia, 1975- (International Society for Music Information Retrieval (ISMIR), 2019)
    Grouping songs together, according to music preferences, mood or other characteristics, is an activity which reflects personal listening behaviours and tastes. In the last two decades, due to the increasing size of music ...
  • A Model for evaluating popularity and semantic information variations in radio listening sessions
    Porcaro, Lorenzo; Gómez Gutiérrez, Emilia, 1975- (CEUR Workshop Proceedings, 2019)
    Listening to music radios is an activity that since the 20th century is part of the cultural habits for people all over the world. While in the case of analog radios DJs are in charge of selecting the music to be ...
  • Assessing the impact of music recommendation diversity on listeners
    Porcaro, Lorenzo (Universitat Pompeu Fabra, 2023-02-06)
    This thesis focuses on assessing the impact that music recommendation diversity may have on listeners. In the music domain, diversity is one of the values that recommender systems should preserve, because the world music ...
  • Diversity by design in music recommender systems
    Porcaro, Lorenzo; Castillo, Carlos; Gómez Gutiérrez, Emilia, 1975- (Ubiquity Press, 2021)
    Music Recommender Systems (Music RS) are nowadays pivotal in shaping the listening experience of people all around the world. Partly driven by the commercial application of this technology, music recommendation research ...
  • Diversity in the music listening experience: insights from focus group interviews
    Porcaro, Lorenzo; Gómez Gutiérrez, Emilia, 1975-; Castillo, Carlos (ACM Association for Computer Machinery, 2022)
    Music listening in today’s digital spaces is highly characterized by the availability of huge music catalogues, accessible by people all over the world. In this scenario, recommender systems are designed to guide listeners ...
  • Emotion annotation of music: a citizen science approach
    Gutiérrez Páez, Nicolás; Gómez Cañón, Juan Sebastián; Porcaro, Lorenzo; Santos Rodríguez, Patrícia; Hernández Leo, Davinia; Gómez Gutiérrez, Emilia, 1975- (Springer, 2021)
    The understanding of the emotions in music has motivated research across diverse areas of knowledge for decades. In the field of computer science, there is a particular interest in developing algorithms to “predict” the ...
  • Exploring artist gender bias in music recommendation
    Shakespeare, Dougal; Porcaro, Lorenzo; Gómez Gutiérrez, Emilia, 1975-; Castillo, Carlos (CEUR Workshop Proceedings, 2020)
    Music Recommender Systems (mRS) are designed to give personalised and meaningful recommendations of items (i.e. songs, playlists or artists) to a user base, thereby reflecting and further complementing individual users’ ...
  • How diverse is the ACII community? Analysing gender, geographical and business diversity of affective computing research
    Hupont, Isabelle; Tolan, Songül; Freire, Ana; Porcaro, Lorenzo; Estevez, Sara; Gómez Gutiérrez, Emilia, 1975- (Institute of Electrical and Electronics Engineers (IEEE), 2021)
    ACII is the premier international forum for presenting the latest research on affective computing. In this work, we monitor, quantify and reflect on the diversity in ACII conference across time by computing a set of indexes. ...
  • Information extraction from user-generated content in the classical music domain
    Porcaro, Lorenzo (2018-09)
    The applications of Information Extraction (IE) on User-generated Content (UGC) have widely benefited from the emergence of microblogging services in the last decade. In particular, Twitter has been at the center of ...
  • Music recommendation diversity: a tentative framework and preliminary results
    Porcaro, Lorenzo; Castillo, Carlos; Gómez Gutiérrez, Emilia, 1975- (International Society for Music Information Retrieval (ISMIR), 2019)
    Music recommendations are increasingly part of the listening experience of people all over the world, especially in the context of streaming services. In this scenario, recommender systems’ role is to help users in ...
  • Perceptions of diversity in electronic music: the impact of listener, artist, and track characteristics
    Porcaro, Lorenzo; Gómez Gutiérrez, Emilia, 1975-; Castillo, Carlos (ACM Association for Computer Machinery, 2022)
    Shared practices to assess the diversity of retrieval system results are still debated in the Information Retrieval community, partly because of the challenges of determining what diversity means in specific scenarios, and ...
  • Recognizing musical entities in user-generated content
    Porcaro, Lorenzo; Saggion, Horacio (Computing Research Center (CIC-IPN), 2019)
    Recognizing Musical Entities is important for Music Information Retrieval (MIR) since it can improve the performance of several tasks such as music recommendation, genre classification or artist similarity. However, ...
  • TROMPA-MER: an open dataset for personalized music emotion recognition
    Gómez Cañón, Juan Sebastián; Gutiérrez Páez, Nicolás; Porcaro, Lorenzo; Porter, Alastair; Cano, Estefanía; Herrera Boyer, Perfecto, 1964-; Gkiokas, Aggelos; Santos Rodríguez, Patrícia; Hernández Leo, Davinia; Karreman, Casper; Gómez Gutiérrez, Emilia (Springer, 2022)
    We present a platform and a dataset to help research on Music Emotion Recognition (MER). We developed the Music Enthusiasts platform aiming to improve the gathering and analysis of the so-called “ground truth” needed as ...