Indicators of Country Similarity in Terms of Music Taste, Cultural, and Socio-economic Factors

Schedl, Markus; Lemmerich, Florian; Ferwerda, Bruce; Skowron, Marcin; Knees, Peter

doi:10.1109/ism.2017.55

Cited by 12 publications

(9 citation statements)

References 13 publications

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“…Although positions are fairly stable over the years, if anything, clusters of countries seem to consolidate, being these three groups more clearly discernible in 2020 than in 2017. At first sight, these results are consistent with general notions of cultural, geographical, and linguistic distance which elsewhere have been proved to be the main determinants of music taste similarities between countries (Pichl et al 2017;Schedl et al 2017;Moore et al 2014).…”

Section: Resultssupporting

confidence: 87%

Cultural Divergence in popular music: the increasing diversity of music consumption on Spotify across countries

Bello¹,

García²

2020

Preprint

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The digitization of music has changed how we consume, produce, and distribute music. In this paper, we explore the effects of digitization and streaming on the globalization of popular music. While some argue that digitization has led to more diverse cultural markets, others consider that the increasing accessibility to international music would result in a globalized market where a few artists garner all the attention. We tackle this debate by looking at how cross-country diversity in music charts has evolved over 7 years in 51 countries. We analyze two large-scale datasets from Spotify, the most popular streaming platform at the moment, and Itunes, one of the pioneers in digital music distribution. Our analysis reveals an upward trend in music consumption diversity that started in 2017 and spans across platforms. There are now significantly more songs, artists, and record labels populating the top charts than just a few years ago, making national charts increasingly different from one another. Furthermore, this process started at the peaks of countries' charts, where diversity increased at a faster pace than at their bases.

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Section: Resultssupporting

confidence: 87%

Cultural Divergence in popular music: the increasing diversity of music consumption on Spotify across countries

Bello¹,

García²

2020

Preprint

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“…The work at hand differentiates from related work in several aspects. First, although music preferences vary across countries, several studies (e.g., Moore et al, 2014 ; Pichl et al, 2017 ; Schedl et al, 2017 ; Bauer and Schedl, 2019 ) have shown similarities in music preferences between countries, typically identified with clustering approaches. Yet, to the best of our knowledge, the work at hand is the first one to integrate information on country similarities into the music recommendation approach.…”

Section: Conceptual Background and Related Workmentioning

confidence: 99%

Listener Modeling and Context-Aware Music Recommendation Based on Country Archetypes

Schedl

Bauer

Reisinger

et al. 2021

Front. Artif. Intell.

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Music preferences are strongly shaped by the cultural and socio-economic background of the listener, which is reflected, to a considerable extent, in country-specific music listening profiles. Previous work has already identified several country-specific differences in the popularity distribution of music artists listened to. In particular, what constitutes the “music mainstream” strongly varies between countries. To complement and extend these results, the article at hand delivers the following major contributions: First, using state-of-the-art unsupervized learning techniques, we identify and thoroughly investigate (1) country profiles of music preferences on the fine-grained level of music tracks (in contrast to earlier work that relied on music preferences on the artist level) and (2) country archetypes that subsume countries sharing similar patterns of listening preferences. Second, we formulate four user models that leverage the user’s country information on music preferences. Among others, we propose a user modeling approach to describe a music listener as a vector of similarities over the identified country clusters or archetypes. Third, we propose a context-aware music recommendation system that leverages implicit user feedback, where context is defined via the four user models. More precisely, it is a multi-layer generative model based on a variational autoencoder, in which contextual features can influence recommendations through a gating mechanism. Fourth, we thoroughly evaluate the proposed recommendation system and user models on a real-world corpus of more than one billion listening records of users around the world (out of which we use 369 million in our experiments) and show its merits vis-à-vis state-of-the-art algorithms that do not exploit this type of context information.

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“…Our sample focused only on participants situated in the United States. Recent work showed that there are cultural differences in music consumption (e.g., [27,37,82,83,90]). Hence, cultural differences may also play a role in taxonomy usage and category preferences.…”

Section: Limitations and Future Workmentioning

confidence: 99%

Personality and taxonomy preferences, and the influence of category choice on the user experience for music streaming services

Ferwerda

Yang

Schedl

et al. 2019

Multimed Tools Appl

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Music streaming services increasingly incorporate different ways for users to browse for music. Next to the commonly used "genre" taxonomy, nowadays additional taxonomies, such as mood and activities, are often used. As additional taxonomies have shown to be able to distract the user in their search, we looked at how to predict taxonomy preferences in order to counteract this. Additionally, we looked at how the number of categories presented within a taxonomy influences the user experience. We conducted an online user study where participants interacted with an application called "Tune-A-Find". We measured taxonomy choice (i.e., mood, activity, or genre), individual differences (e.g., personality traits and music expertise factors), and different user experience factors (i.e., choice difficulty and satisfaction, perceived system usefulness and quality) when presenting either 6-or 24-categories within the picked taxonomy. Among 297 participants, we found that personality traits are related to music taxonomy preferences. Furthermore, our findings show that the number of categories within a taxonomy influences the user experience in different ways and is moderated by music expertise. Our findings can support personalized user interfaces in music streaming services. By knowing the user's personality and expertise, the user interface can adapt to the user's preferred way of music browsing and thereby mitigate the problems that music listeners are facing while finding their way through the abundance of music choices online nowadays.

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Indicators of Country Similarity in Terms of Music Taste, Cultural, and Socio-economic Factors

Cited by 12 publications

References 13 publications

Cultural Divergence in popular music: the increasing diversity of music consumption on Spotify across countries

Cultural Divergence in popular music: the increasing diversity of music consumption on Spotify across countries

Listener Modeling and Context-Aware Music Recommendation Based on Country Archetypes

Personality and taxonomy preferences, and the influence of category choice on the user experience for music streaming services

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