Music recommendation has been a core area of interest within the recommender systems community since its early days. With the rise of music streaming platforms, algorithmic recommendations have become central to the music industry. However, critical challenges remain concerning the design, evaluation, and societal impact of music recommender systems. This third edition of the Music Recommender Systems Workshop (MuRS) centers on the growing influence of generative content on music recommendation. The rapid influx of AI-generated music transforms the streaming ecosystem, raising critical questions about discoverability, authenticity, and the curational role of recommender systems. The challenges and opportunities associated with AI-generated content extend beyond music and recommender systems, demanding transparent, fair, and accountable recommendation frameworks that reflect the interests of the diverse set of stakeholders.

The European Union (EU) Digital Services Act (DSA) has introduced a novel set of rules for online platforms and search engines, with significant implications for the Recommender Systems community. Through its data access mechanisms, the DSA invites researchers to request both publicly available and private data from Very Large Online Platforms (VLOPs) and Very Large Search Engines (VLOSEs) – those with more than 45 million active recipients in the EU – to investigate systemic risks associated with the dissemination of illegal content, risks to the exercise of fundamental rights, and negative effects on electoral processes, public health, and gender-based violence. This tutorial is aimed at researchers who are interested in submitting such data access requests and will provide them with the knowledge to do so by introducing the relevant definitions and provisions of the DSA, and addressing the most important procedural steps to obtain data access and will provide attendees with a comprehensive understanding of the DSA’s data access implications for RecSys research. The tutorial targets researchers, practitioners, and students in understanding current developments in online platform regulation in Europe and their impact on RecSys research.

Music recommendation has been relevant to the Recommender Systems (RecSys) community since the early days. With the growth of music streaming platforms, algorithmic recommendations have become critical in the music industry. However, many challenges are still wide open in the area of music recommender systems. Such challenges are currently being addressed in several research communities, including and beyond the RecSys and the Music Information Retrieval (MIR) communities. The RecSys conference has traditionally not focused very much on music content understanding. In contrast, while music content understanding is central to the MIR community, research on recommender systems is not prominent in MIR research. The Music Recommender Systems Workshop (MuRS) aims at bridging the existing gap between the diverse research communities focused on the specific challenges of music recommender systems. The workshop provides a space for researchers and practitioners from multiple disciplines to jointly discuss and exchange perspectives and solutions, and to promote discussion from both academia and industry upon future research directions in the area of music recommender systems.

We present the results of a 12-week longitudinal user study wherein the participants, 110 subjects from Southern Europe, received on a daily basis Electronic Music (EM) diversified recommendations. By analyzing their explicit and implicit feedback, we show that exposure to specific levels of music recommendation diversity may be responsible for long-term impacts on listeners’ attitudes. In particular, we highlight the function of diversity in increasing the openness in listening to EM, a music genre not particularly known or liked by the participants previous to their participation in the study. Moreover, we demonstrate that recommendations may help listeners in removing positive and negative attachments towards EM, deconstructing pre-existing implicit associations but also stereotypes associated with this music. In addition, our results show the significant influence that recommendation diversity has in generating curiosity in listeners.

Abstract 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 input to MER systems. Firstly, our platform involves engaging participants using citizen science strategies and generate music emotion annotations – the platform presents didactic information and musical recommendations as incentivization, and collects data regarding demographics, mood, and language from each participant. Participants annotated each music excerpt with single free-text emotion words (in native language), distinct forced-choice emotion categories, preference, and familiarity. Additionally, participants stated the reasons for each annotation – including those distinctive of emotion perception and emotion induction. Secondly, our dataset was created for personalized MER and contains information from 181 participants, 4721 annotations, and 1161 music excerpts. To showcase the use of the dataset, we present a methodology for personalization of MER models based on active learning. The experiments show evidence that using the judgment of the crowd as prior knowledge for active learning allows for more effective personalization of MER systems for this particular dataset. Our dataset is publicly available and we invite researchers to use it for testing MER systems.

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. Albeit several works have proposed new techniques for developing diversity-aware recommendations, 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 a previous quantitative 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. We report here our main findings on 1) what can influence the diversity assessment of tracks and artists’ music lists, and 2) which factors can characterize listeners’ interaction with music recommendation diversity.