Large-Scale Analysis of Group-Specific Music Genre Taste from Collaborative Tags

Schedl, Markus; Ferwerda, Bruce

doi:10.1109/ism.2017.95

Cited by 21 publications

(20 citation statements)

References 10 publications

(9 reference statements)

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“…To exemplify also negative outliers, Table 8 shows for the United States, the first (highest global position) positive and negative outliers that appear along the trend when using the AF measure. Among the negative outliers, we find mostly hard rock and metal bands, which corroborates previous findings that these genres are underrepresented in the United States compared to the global mean [42].…”

Section: Analysis Of Global Versus Country-specific Mainstreamsupporting

confidence: 90%

An Analysis of Global and RegionalMainstreaminess for Personalized MusicRecommender Systems

Schedl¹,

Bauer²

2018

JMM

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The music mainstreaminess of a listener reflects how strong a person's listening preferences correspond to those of the larger population. Considering that music mainstream may be defined from different perspectives, we show country-specific differences and study how taking into account music mainstreaminess influences the quality of music recommendations. In this paper, we first propose 11 novel mainstreaminess measures characterizing music listeners, considering both a global and a countryspecific basis for mainstreaminess. To this end, we model preference profiles (as a vector over artists) for users, countries, and globally, incorporating artist frequency, listener frequency, and a newly proposed TF-IDF-inspired weighting function, which we call artist frequency-inverse listener frequency (AF-ILF). The resulting preference profile for each user u is then related to the respective country-specific and global preference profile using fraction-based approaches, symmetrized Kullback-Leibler divergence, and Kendall's τ rank correlation, in order to quantify u's mainstreaminess. Second, we detail country-specific peculiarities concerning what defines the countries' mainstream and discuss the proposed mainstreaminess definitions. Third, we show that incorporating the proposed global and country-specific

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Section: Analysis Of Global Versus Country-specific Mainstreamsupporting

confidence: 90%

An Analysis of Global and RegionalMainstreaminess for Personalized MusicRecommender Systems

Schedl¹,

Bauer²

2018

JMM

Self Cite

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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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“…The LFM-1b dataset may not necessarily generalize to the population at large. For instance, the usage of Last.fm data introduces a community bias [82]. In terms of age distribution, it may also not be representative to the global population at large [83].…”

Section: Datasetmentioning

confidence: 99%

Global and country-specific mainstreaminess measures: Definitions, analysis, and usage for improving personalized music recommendation systems

Bauer

Schedl

2019

PLoS ONE

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Relevance Popularity-based approaches are widely adopted in music recommendation systems, both in industry and research. These approaches recommend to the target user what is currently popular among all users of the system. However, as the popularity distribution of music items typically is a long-tail distribution, popularity-based approaches to music recommendation fall short in satisfying listeners that have specialized music preferences far away from the global music mainstream. Addressing this gap, the contribution of this article is three-fold. Definition of mainstreaminess measures First, we provide several quantitative measures describing the proximity of a user’s music preference to the music mainstream. Assuming that there is a difference between the global music mainstream and a country-specific one, we define the measures at two levels: relating a listener’s music preferences to the global music preferences of all users, or relating them to music preferences of the user’s country. To quantify such music preferences, we define a music item’s popularity in terms of artist playcounts (APC) and artist listener counts (ALC). Moreover, we adopt a distribution-based and a rank-based approach as means to decrease bias towards the head of the long-tail distribution. This eventually results in a framework of 6 measures to quantify music mainstream. Differences between countries with respect to music mainstream Second, we perform in-depth quantitative and qualitative studies of music mainstream in that we (i) analyze differences between countries in terms of their level of mainstreaminess, (ii) uncover both positive and negative outliers (substantially higher and lower country-specific popularity, respectively, compared to the global mainstream), analyzing these with a mixed-methods approach, and (iii) investigate differences between countries in terms of listening preferences related to popular music artists. We conduct our studies and experiments using the standardized LFM-1b dataset, from which we analyze about 800,000,000 listening events shared by about 53,000 users (from 47 countries) of the music streaming platform Last.fm. We show that there are substantial country-specific differences in listeners’ music consumption behavior with respect to the most popular artists listened to. Rating prediction experiments Third, we demonstrate the applicability of our study results to improve music recommendation systems. To this end, we conduct rating prediction experiments in which we tailor recommendations to a user’s level of preference for the music mainstream using the proposed 6 mainstreaminess measures: defined by a distribution-based or rank-based approach, defined on a global level or on a country level (for the user’s country), and for APC or AL...

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Large-Scale Analysis of Group-Specific Music Genre Taste from Collaborative Tags

Cited by 21 publications

References 10 publications

An Analysis of Global and RegionalMainstreaminess for Personalized MusicRecommender Systems

An Analysis of Global and RegionalMainstreaminess for Personalized MusicRecommender Systems

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

Global and country-specific mainstreaminess measures: Definitions, analysis, and usage for improving personalized music recommendation systems

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