if you have landed here, it means that you are interested in Electronic Music, Diversity, Music Recommender Systems or a combination of those.

If so, you will find few insights from the work done on music recommendation diversity by Lorenzo Porcaro, PhD student at the Music Technology Group (UPF), in collaboration with Emilia Gómez (Joint Research Center, European Commission) and Carlos Castillo (Web Science and Social Computing Group, UPF & ICREA).

The info in this page comes from two works published and to be presented soon:
¬ Porcaro, L., Gómez, E., & Castillo, C. (2022). Perceptions of Diversity in Electronic Music: the Impact of Listener, Artist, and Track Characteristics . In CSCW ’22: ACM Conference on Computer-Supported Cooperative Work and Social Computing, November 12–16, 2022, Taipei, Taiwan.
¬ 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.

You can read the preprint in arXiv (together with other papers around these topics).

Perceptions of Diversity in Electronic Music: the Impact of Listener, Artist, and Track Characteristics.

Shared practices to assess the diversity of retrieval system (e.g. search engines, recommender systems) results are still debated in the Information Retrieval community, partly because of the challenges of determining what diversity means in specific scenarios, and of understanding how diversity is perceived by end-users.

The field of Music Information Retrieval is not exempt from this issue. Even if fields such as Musicology or Sociology of Music have a long tradition in questioning the representation and the impact of diversity in cultural environments, such knowledge has not been yet embedded into the design and development of music technologies.

Focusing on electronic music, we investigate the characteristics of listeners, artists, and tracks that are influential in the perception of diversity. Specifically, we center our attention on 1) understanding the relationship between perceived diversity and computational methods to measure diversity, and 2) analyzing how listeners' domain knowledge and familiarity influence such perceived diversity.

We design a user-study wherein listeners are asked to compare pairs of lists of tracks and artists, and to select the most diverse list from each pair. We compare participants’ ratings with results obtained through computational models built using audio tracks' features and artist attributes.

We find that such models are generally aligned with participants' choices when most of them agree that one list is more diverse than the other. In addition, we observe how differences in domain knowledge, familiarity, and demographics influence the level of agreement among listeners, and between listeners and computational diversity metrics.









Diversity in the Music Listening Experience: Insights from Focus Group Interviews

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 in finding tracks and artists that best fit their requests, having therefore the power to influence the diversity of the music they listen to.

Several works have proposed new techniques for developing diversity-aware recommendations, but little is known about how people perceive diversity while interacting with music recommendations. In this study, we interview several listeners about the role that diversity plays in their listening experience, trying to get a better understanding of how they interact with music recommendations.

We recruit the listeners among the participants of our previous study, where they were confronted with the notion of diversity when asked to identify, from a series of electronic music lists, the most diverse ones according to their beliefs. As a follow-up, in this qualitative study we carry out semi-structured interviews to understand how listeners may assess the diversity of a music list and to investigate their experiences with music recommendation diversity.

Insights from Interviews



Listen to the survey's tracks in YouTube.


Listen to the survey's tracks in Spotify.


Listen to the survey's tracks in in SoundCloud.


Support the artists shown in the survey via Bandcamp.


Discover more about the Music Technology Group in Barcelona.


Discover more about TROMPA, research project sponsored by the EU.

Frequently Asked Questions

This project has received funding from the European Union's Horizon 2020 research and innovation programme H2020-EU. - Study European heritage, memory, identity, integration and cultural interaction and translation, including its representations in cultural and scientific collections, archives and museums, to better inform and understand the present by richer interpretations of the past under grant agreement No 770376.