# [Preprint version] Listener Modeling and Context-aware Music Recommendation
Based on Country Archetypes

Markus Schedl<EMAIL_ADDRESS>Johannes Kepler University Linz, Institute
of Computational Perception, Multimedia Mining and Search GroupLinz Institute
of Technology, AI Lab, Human-centered AI GroupLinzAustria ,  Christine Bauer
<EMAIL_ADDRESS>Utrecht UniversityUtrechtThe Netherlands ,  Wolfgang Reisinger
Johannes Kepler University Linz, Institute of Computational Perception,
Multimedia Mining and Search GroupLinzAustria ,  Dominik Kowald  Graz
University of Technology and Know-CenterGrazAustria and  Elisabeth Lex  Graz
University of Technology and Know-CenterGrazAustria

###### Abstract.

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 unsupervised 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.

††copyright: none

##  1\. Keywords:

music, recommender system, culture, country, clustering, context, user
modeling, music preferences.

##  2\. Introduction

Recommendation systems (or recommender systems) have become an important means
to help users find and discover various types of content and goods, including
movies, videos, books, and food (Ricci et al., 2015). As such, they represent
substantial business value. In the music industry, recommender systems—powered
by machine learning and artificial intelligence—have radically changed the
market; they have even become major drivers in this industry. Essentially,
music recommender systems (MRS) shape today’s digital music distribution
(Schedl et al., 2015) and have become vital tools for marketing music to a
targeted audience, as evidenced by the success of recommender-systems-
featuring music streaming services such as Spotify, Deezer, or Apple Music.
While MRS operate in a multi-stakeholder environment including platform
providers, artists, record companies, and music consumers/listeners (Bauer and
Zangerle, 2019), it is most commonly the music consumers/listeners, who are
considered the users of an MRS. In the paper at hand, we also take this
perspective.

Traditionally, content-based filtering (CBF) and collaborative filtering
(CF)—or hybrid combinations thereof—have been the most common algorithms to
create recommender systems (Ricci et al., 2015). The former assumes that users
will like items similar to the ones they liked in the past, and therefore
selects items to recommend according to some notion or metric of similarity in
terms of item content (e.g., music style, timbre, or rhythm) between the
user’s liked items and unseen items from the catalog. In contrast, CF assumes
that a user will prefer items that are liked by other users with similar
preferences. In this case, items to recommend are, for instance, found by
comparing the target user’s consumption or rating profile to that of the other
users, identifying the most similar other users, and recommending what they
liked (user-based CF). Alternatively, users and items can be directly matched
via similarities computed in a joint low-dimensional representation of users
and items (i.e., model-based CF).

Enhancing the classical approaches CF and CBF, in recent years, researchers
started to leverage additional information—beyond users, items, and their
interactions—to improve recommendations. Recommender systems that consider
user characteristics or information describing a situation are typically
referred to as _context-aware recommendation systems_ (Adomavicius and
Tuzhilin, 2015). Next to considering time and location as contextual side
information, taking information derived from the user’s country into account
has been demonstrated to improve recommendation quality; for instance,
cultural and socio-economic characteristics of the user’s country (Zangerle et
al., 2018), or the user taste’s proximity to their country-specific music
mainstream (“mainstreaminess”) (Bauer and Schedl, 2019).

Against this background, we approach the task of context-aware music
recommendation based on country information; in contrast to most previous
works, we consider user country in our approach without using any external
information about the country, such as cultural, economic, or societal
information. The reason is that respective data sources about countries (e.g.,
Hofstede’s cultural dimensions,111https://geerthofstede.com/research-and-
vsm/dimension-data-matrix the Quality of Government
measures,222https://qog.pol.gu.se or the World Happiness
Report333https://worldhappiness.report) provide information on the country
level, which may not necessarily reflect the circumstances of individual users
and, thus, can introduce problems in the recommendation process. For instance,
cultural values or income may be very unequally distributed among a country’s
population.

To avoid this, instead of using external information derived from the user’s
country, we leverage purely the self-reported country information of the users
as available in the system, and investigate how behavioral data about music
listening can be used to (1) identify archetypal country clusters based on
track listening preferences, (2) how users can be modeled using the results of
(1), and (3) how the resulting user models can be integrated into a state-of-
the-art deep learning-based music recommendation algorithm.

As in many other domains, nowadays, deep neural network architectures dominate
research in music recommendation systems, due to their ability to
automatically learn features from low-level audio signals and their superior
performance (Schedl, 2019). This article is no exception. We propose a multi-
layer generative model in which contextual features can influence
recommendations through a gating mechanism.

In this context, we formulate the following research questions:

RQ1: To what extent can we identify and interpret groups of countries that
constitute music preference archetypes, from behavioral traces of users’ music
listening records?

RQ2: Which are effective ways to model the users’ geographic background as a
contextual factor for music recommendation?

RQ3: How can we extend a state-of-the-art recommendation algorithm, based on
variational autoencoders, to include user context information, in particular,
the geo-aware user models developed to answer RQ2?

In the remainder of this article, we first explain the conceptual foundation
of our work and discuss it in the context of related research (Section 3).
Subsequently, we detail the methods we adopt to investigate the research
questions; in particular, we specify the approaches used for data preparation,
clustering, user modeling, and track recommendation (Section 4). The results
of our experiments on uncovering geographic music listening archetypes and on
music track recommendation, altogether with a detailed discussion thereof, are
presented in Section 5. Finally, Section 6 concludes the article with a brief
summary of the major findings, a discussion of limitations, and pointers to
future work.

##  3\. Conceptual Background and Related Work

A multitude of factors have been found to influence an individual’s music
preferences. There is a long history of research investigating the
relationships between music preferences and, for instance, demographics
(Bonneville-Roussy et al., 2013; Colley, 2008; Cheng et al., 2017),
personality traits (Rentfrow and Gosling, 2003; Schäfer and Mehlhorn, 2017),
and social influences (ter Bogt et al., 2011; Bonneville-Roussy and Rust,
2018) .

In the middle of the nineteenth century emerged a cultural hierarchy in
America (DiMaggio, 1982; Levine, 1988) where a high social status patronized
the fine arts (referred to as “highbrow”) while all other forms of popular
culture were associated with a lower status (referred to as ”middlebrow” or
“lowbrow”). In the 1990s, a series of studies (Peterson and Simkus, 1992;
Peterson and Kern, 1996) have defended the view that, for the elite, highbrow
was being replaced by a consumption pattern termed “omnivorousness”. Cultural
omnivorousness reflects that people’s taste includes both elite and popular
genres. This was subsequently shown to hold for various countries (e.g.
Coulangeon, 2003; Fisher and Preece, 2003; Holbrook et al., 2002). Also, the
consumption practices of low status taste were reconceptualized: The earlier
view that the lowbrow group would be willing to consume any entertainment on
offer (Horkheimer and Adorno, 1972) was replaced by the finding that low
status people tend to choose one form of entertainment and avoid others
(Bryson, 1997). Thus, overall the view evolved from highbrow–lowbrow to
omnivore–univore. Analyzing music consumption across eight European countries,
Coulangeon and Roharik (2005) supported the “omnivore–univore” scheme rather
than the former “highbrow–lowbrow” model. The omnivorous cultural taste was
later found unstable over time (Rossman and Peterson, 2015), though. Katz-
Gerro (2002) has shown that the dividing line of class distinctions varies
across countries and also the genre associations to social classes deviate.
She concludes that, while class matters, the main determinants of cultural
preferences relate to gender, education, and age (Katz-Gerro, 1999).
Coulangeon (2005) questions the earlier view on the reasons for the different
tastes of higher- and lower-status classes: He challenges that it would be the
upper class’ familiarity with the so-called “legitimate” culture and the
little accessibility to that culture for the lower-status classes, that
distinguished what the upper class and lower-status classes prefer. Instead,
he attributes it to the diversity of the stated preferences of people of the
upper class, whereas the preferences of members of lower-status classes appear
more exclusive. Later work, studying music taste in the “modern age” (Nuccio
et al., 2018), found little evidence that musical taste is indeed aligned with
class position.

Although there is a multitude of factors that influence an individual’s music
preferences that lead to a diversity of music created and listened to, there
are (market) structures and other mechanisms that effect certain tendencies in
what music is preferred within a particular community. For instance,  the
music recording industry is typically considered a globally oriented market
(Dolata, 2013). Yet, studies have revealed the existence of national
boundaries (Bauer and Schedl, 2018). There are various country-specific
mechanisms that affect an individual’s music preferences and consumption
behavior: Preferences are culturally shaped (Baek, 2015; Budzinski and
Pannicke, 2017); music perceptions vary across cultures, for instance, with
respect to mood (Lee and Hu, 2014; Morrison and Demorest, 2009; Singhi and
Brown, 2014; Stevens, 2012); and countries have substantially different
national market structures with respect to, for instance, available music
repertoire due to copyright and licensing, advertising campaigns, local radio
airplay, or quotas for national artists (Hracs et al., 2017; Gomez Herrera and
Martens, 2018).

Knowledge about country-specific differences in music preferences can be
explicitly used to improve music recommender systems, for instance, by
leveraging information about the users’ geographic or cultural background. For
instance, Vigliensoni and Fujinaga (2016) use a factorization machine approach
for matrix factorization and singular value decomposition to integrate—amongst
others—a user’s country as context information. Bauer and Schedl (2019) use a
contextual pre-filtering approach (Adomavicius and Tuzhilin, 2015), where the
user base is first segmented by user country, and a target person is then
compared to other people from the very same country (in contrast to a
comparison with the entire user base). Sánchez-Moreno et al. (2016) use a
k-nearest neighbor (k-NN) approach integrating, amongst others, the user’s
country as attribute. Zangerle et al. (2018) leverage further country-specific
data sources; for each country, they use the respective scores on the cultural
dimensions by Hofstede (Hofstede et al., 2005) as well as the scores of the
World Happiness Report (Helliwell et al., 2016) to tailor recommendations to
the individual.

The work at hand differentiates from related work in several aspects.

  * •

First, although music preferences vary across countries, several studies (e.g.
Bauer and Schedl, 2019; Pichl et al., 2017; Schedl et al., 2017; Moore et al.,
2014) 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.

  * •

Second, while other work, most notably, Zangerle et al. (2018), reaches out to
include external data about countries (such as economic factors, happiness
index, cultural dimensions), the approach at hand remains independent from any
external data sources, enabling platform providers to build a self-sustaining
recommendation system. Such a system can rely exclusively on data that is
contained in the provider’s platform, including users’ self-disclosed country
information.

  * •

Third, most existing research on music preferences and recommender systems
considers music preferences on a genre level (e.g. Skowron et al., 2017;
Adiyansjah et al., 2019) or artist level (e.g. Bauer and Schedl, 2019;
Sánchez-Moreno et al., 2016). Research on country-aware music recommendation
systems that provide recommendations on the track level is rare (e.g. Zangerle
et al., 2018). However, the genre and the artist level may be too coarse-
grained to reflect users’ music preferences, for several reasons. Music genres
are vaguely defined (Beer, 2013; Sonnett, 2016; Vlegels and Lievens, 2017) and
users’ perceptions thereof differ tremendously (Brisson and Bianchi, 2019; van
Venrooij, 2009). Artists frequently cover several music styles throughout
their career, where some tracks may be more favored than others for reasons
including lyrics quality, the influence of associated music videos, over-
exposure, or associations with unpleasant personal experiences (Cunningham et
al., 2005). Accordingly, the work at hand investigates music recommendations
on the track level to reflect users’ preferences in a more fine-grained manner
than genre labels attributed to an artist’s overall repertoire could do.

  * •

Fourth, while deep learning approaches are increasingly used for recommender
systems in general and for music recommendation in particular, the integration
of geographic aspects—especially user country—with deep learning for music
recommendation is a particular asset of the work at hand. For instance, a
recent survey on deep learning-based recommender systems (Batmaz et al., 2019)
reports that extant research mainly uses textual information to capture
context in approaches to context-aware recommender systems. The authors
particularly consider context that is extracted from items (e.g., text
documents) instead of users.

##  4\. Methods

In the following, we detail how we gather and process the dataset used in our
study, which contains information about users’ music listening behavior
(Section 4.1). We then describe our approach to identify country clusters
based on this dataset (Section 4.2). Finally, we elaborate on our approaches
to create user models incorporating country information and we detail our
neural network architecture that integrates these models (Section 4.3).

###  4.1. Data Acquisition and Processing

We base our investigations on the LFM-1b dataset (Schedl, 2016), which we
filter according to our requirements as detailed below. The LFM-1b
dataset444http://www.cp.jku.at/datasets/LFM-1b contains music listening
information for 120,322 Last.fm users, totaling to 1,088,161,692 individual
listening events (LEs) generated between January 2005 and August 2014; the
majority of LEs was created during years 2012–2014.555The LFM-1b dataset used
in our study is considered derivative work according to paragraph 4.1 of
Last.fm’s API Terms of Service (https://www.last.fm/api/tos). The Last.fm
Terms of Service further grant us a license to use this data (according to
paragraph 4). Each LE is characterized as a quintuple of user-id, artist-id,
album-id, track-id, and timestamp. The average number of LEs per user in the
dataset is 8,879 (std. 15,962). For some users, also demographic data
(country, age, and gender) is available in LFM-1b. More precisely, 46% of the
users do provide information about their country, the same percentage do
provide information about their gender, and 62% about their age. The majority
of users who provide their country are from the US (18.5%), followed by Russia
(9.1%), Germany (8.3%), the UK (8.3%), Poland (8.0%), Brazil (7.0%), and
Finland (2.6%). The mean age of the users who reveal it is 25.4 years (std.
9.4); the median age is 23 years. The age distribution differs significantly
between countries, though. In Figure 1, we show the age distribution for the
countries with at least 100 users (47 countries), categorized into age groups.
The youngest users are found in Estonia and Poland, while the oldest users are
Swiss and Japanese. Among the users who indicate their gender, 72% are male
and 28% are female. These percentages differ, however, considerably between
countries. In Figure 2, we therefore depict the ratios between genders, again
for the top 47 countries in terms of number of users. While the Baltic
countries Lithuania and Latvia have an almost equal share of male and female
users, India and Iran show a very unequal distribution (around 90% male
users).

Figure 1. Distribution of age over countries. Countries are sorted in
decreasing order of number of users from left to right. Figure 2. Distribution
of gender over countries. Countries are sorted in decreasing order of number
of users from left to right.

As reported above, about 46% of users in the LFM-1b dataset disclose their
country. For our country-specific analysis, we therefore only consider users
(and their LEs) for whom country information is available. This results in a
dataset of 55,186 users, who have listened to a total of 26,021,362 unique
tracks. The distribution of the number of LEs over tracks is visualized in
Figure 3.

Figure 3. Distribution of number of listening events over all tracks (semi-
log-scaled). Track identifiers are ordered by number of LEs.

We subsequently reduce the data to decrease noise originating from the user-
generated nature of the metadata in the LFM-1b dataset (in particular,
misspellings and ambiguities), i.e., we filter out tracks and countries. This
noise would otherwise likely cause distortions in future steps of our
approach. First, we drop tracks that have been listened to less than 1,000
times, globally, resulting in a total of 122,442 tracks to consider further.
Second, to minimize possible distortions caused by countries with a low number
of LEs or a low number of unique users, we only consider countries with at
least 80,000 LEs and at least 25 users. We chose these values as thresholds
based on an empirical investigation of the distributions of LEs and of users
over countries (cf. Figures 4 and 5, respectively). The former shows a flat
characteristic around country-id 100, followed by a clear gap between country-
id 110 and 111 (which corresponds to 80,000 LEs). The latter reveals a sudden
drop at country-id 70 (which corresponds to 25 users). Applying this country
filtering eventually results in 70 unique countries and a total of 369,290,491
LEs, which represents only a small drop of 1.5% (in comparison to 374,770,382
LEs created by users of all countries in the dataset). After these
preprocessing steps, each country is represented as a 122,442-dimensional
feature vector containing the LEs over all tracks.

Figure 4. Distribution of number of listening events over all countries (semi-
log-scaled). Country identifiers are ordered by number of LEs. Figure 5.
Distribution of number of users over all countries (semi-log-scaled). Country
id ordered by number of users.

###  4.2. Identifying Country Clusters and Archetypes

To cluster countries according to their citizens’ listening behavior, it is
important to first normalize the data of each country to avoid distortions
caused by different country sizes. To this end, we normalize each country’s
feature vector to sum up to one.666Please note that country-specific results
may still be influenced by some users showing particularly high playcounts.
Nevertheless, we decided against excluding or penalizing the listening
information of such users just because users with a high playcount indicate a
more pronounced inclination to listen to music. Our reasoning is that users
who contribute only few listening events to Last.fm should be considered less
important to model their country-specific listening behavior than users who
heavily contribute. In addition, removing such “power listeners” would distort
the original distribution of users’ playcounts in the sample. We next apply
truncated SVD/PCA (Halko et al., 201), reducing the dimensionality of the
feature vectors to 100, while still preserving 99.8% of the variance in the
data.777Reducing the dimensionality of the dataset to 50 dimensions preserves
only 90.1% of the variance. Taking these 100-dimensional feature vectors as an
input to a t-distributed Stochastic Neighbor Embedding (t-SNE) (van der Maaten
and Hinton, 2008) and subsequently using OPTICS (Ankerst et al., 1999) enables
us to visualize the data and identify clusters of countries sharing similar
music listening behaviors.

T-SNE is a visualization technique that embeds high-dimensional data in a low-
dimensional (typically, two-dimensional) visualization space, paying
particular attention to preserving the local structure of the original data.
It is particularly useful to disentangle data points that lie on more than one
manifold. T-SNE represents proximities or affinities between pairs of data
items by estimating the probability that the first data item will choose the
second one as its nearest neighbor, and vice versa. In the original data
space, this probability is modeled by means of a Gaussian distribution
centered around each data item in the high-dimensional space; in the
visualization space by means of a t-student distribution centered around each
data item in the low-dimensional space. Kullback-Leibler divergence of the
joint distributions between pairs of data points in the original space and in
the visualization space is then minimized via gradient descent.

OPTICS (Ordering Points To Identify the Clustering Structure) is a density-
based clustering method that creates a linear ordering of data items based on
their spatial proximity. For this purpose, OPTICS first identifies core data
points that have at least a certain number of neighbors in their vicinity (the
minimum cluster size) and assigns a core distance to them, describing how
dense the area around each core point is. Furthermore, a reachability distance
between each pair of data items $o$ and $p$ is established, which is the
maximum of (1) the distance between $o$ and $p$ and (2) the core distance of
$o$, whichever is bigger. Data items assigned to the same cluster have a lower
reachability distance to their nearest neighbors than items that belong to
different clusters. OPTICS subsequently creates an ordering of data items in
terms of their reachability distance and identifies sudden changes in
reachability between neighboring items, assuming that these correspond to
cluster borders. The number of clusters is controlled by a parameter $\xi$
that defines the minimum steepness (relative change in distance) between
neighboring data items to be considered a cluster boundary.888In this work, we
use Euclidean distance as distance metric and set $\xi=0.05$.

As for parameter optimization, we adopt a grid search strategy to identify a
well-suited perplexity for t-SNE (5) and a minimum size of clusters, i.e.,
minimum number of data items in each cluster, for OPTICS (3).999More
precisely, we performed grid search on t-SNE perplexity in the range [1, 2, 3,
5, 10, 15, 20, 25, 30, 35, 40, 50] and on the minimum number of data points
per cluster enforced by OPTICS in the range [2, 3, 4, 5], optimizing for
average neighborhood preservation ratio (nearest neighbor consistency). Please
note that we use ISO 3166 2-digit country codes to refer to countries in this
article101010https://www.iso.org/iso-3166-country-codes.html.

For an analysis of the identified clusters in a way that enables the
establishment of archetypes of music preferences, we adopt the following
approach. As shown in Figure 3, we observe a long-tail distribution of
listening events over tracks, which means that a few dominating tracks are
listened to by a lot of users, while most tracks are only listened to by a few
users. Thus, these dominating tracks will also be popular among the list of
top-tracks per cluster, which makes it hard to distinguish between the
clusters and to interpret their corresponding archetypes. To overcome this, we
adapt a scoring function similar to the inverse document frequency (IDF)
(Jones, 1972) metric from the field of information retrieval, which assigns
high scores to rarely occurring tracks and low scores to frequently occurring
tracks. Formally, we define IDF for each track $t_{i}$ as
$IDF(t_{i})=\log_{10}\frac{N}{n_{i}}$, where $N$ is the number of all
listening events and $n_{i}$ is the number of LEs for track $t_{i}$. The
distribution of IDF values of the top 50 tracks, in terms of $IDF(t_{i})$, is
plotted in Figure 6. In an empirical analysis, we identify 10 overall
dominating tracks using a threshold of 4.2 on the IDF values (see Figure 6).
These tracks are Rolling in the Deep by Adele, Somebody That I Used to Know by
Gotye, Islands and Intro by The xx, Blue Jeans by Lana Del Rey, Supermassive
Black Hole by Muse, Skinny Love by Bon Iver as well as Use Somebody, Sex on
Fire and Close by Kings of Leon. We remove these tracks from further analyses
when discussing archetypes as these are not suited to discriminate between
clusters.

Figure 6. Inverse document frequency (IDF) scores for the top 50 tracks.

In our analysis of archetypes, we include genre annotations, which we obtain
as follows. For all tracks in the dataset, we retrieve the top user-generated
tags using the Last.fm API.111111https://www.last.fm/api/show/track.getTopTags
Subsequently, we filter the tags of each track using a comprehensive list of
music genres and styles from Spotify, called Spotify microgenres (Johnston,
2018). This list contains 3,034 genre names (as of May 2019 when we extracted
them), including umbrella genres such as pop and country, as well as smaller
niches such as Thai hip-hop, German metal, and discofox (Johnston, 2018). The
fine-grained reflection of subtle differences in microgenres provides a more
particularized basis for describing the clusters, compared to the use of a
more coarse-grained taxonomy of music genres. We note as a limitation that the
microgenre categories are defined in a similarly vague manner as coarse-
grained taxonomies of music genre (Beer, 2013; Sonnett, 2016; Vlegels and
Lievens, 2017); and the semantics associated with (micro)genre names have
evolved over time so that a precise definition appears difficult. Relying on a
big corpus of data where microgenres are visualized and sonified (see The
Every Noise project121212http://everynoise.com), we nevertheless believe that
using the concept of microgenres helps future research to build upon our work.
Further note that we rely on the top user-generated tags from the Last.fm
community for attributing microgenres to tracks; the microgenre–track
associations, thus, reflect the Last.fm community’s understanding of
microgenres, which may not be congruent with the music experts’ understanding.
Additionally, synonyms may be present in the user-generated tags and, thus,
two different tags could be used interchangeably to annotate the same tracks
(e.g., “Rap” and “HipHop”).

To allow interested readers to conduct further analyses of the identified
clusters on a microgenre level, we release the full list of the top 20 tracks
(and corresponding artists) per cluster, and we include—for each track and
artist—all microgenre annotations (cf. “Accompanying Resources”).

###  4.3. User Modeling and Music Track Recommendation

We build our context-aware music recommendation approach on top of a
variational autoencoder (VAE) model (Jordan et al., 1999). VAEs are a type of
autoencoders (Kramer, 1991) that consist of an encoder, a decoder, and a loss
function. In contrast to classic autoencoders, which learn encodings directly,
VAEs learn the distribution of encodings using variational inference. Via
sampling from the learned distribution, more representations of the same items
can be generated given the same amount of training data. Thus, VAEs can learn
more complex items than classic autoencoders.

We opted to extend the VAE architecture for collaborative filtering presented
by Liang et al. (2018) because in a large-scale study conducted by Dacrema et
al. (2019), the approach followed by Liang et al. (2018) was found the only
deep neural network-based approach that outperformed equally well tuned non-
deep-learning approaches. In addition, Liang et al. (2018) evaluated their VAE
architecture on the Million Song Dataset (Bertin-Mahieux et al., 2011), a
common benchmark in the music domain. They showed substantially superior
performance compared to several baselines, in particular, the linear model
weighted matrix factorization (WMF) and collaborative denoising autoencoders
(CDAE).

As depicted in Figure LABEL:fig:vae_context_architecture, we extend the VAE
architecture by integrating context information using a gating mechanism. The
gate output modulates the latent code in a way to incorporate context-based
(country and cluster) differences of users. The abstract concepts are weighted
based on how important the models deem them for a specific user group.131313We
also run experiments in which we simply concatenate track listening history
and context information, but this did not show improvements over the VAE based
on just the listening history. Specifically, we model users in form of a
122,442-dimensional listening vector (i.e., $n\\_tracks$), which represents
their track listening history, together with context information. We
investigate four different ways to define a user’s context: (1) the user’s
country, (2) the cluster membership of the user’s country, (3) the Euclidean
distances between the user’s listening vector and all identified cluster
centroids, and (4) the Euclidean distances between the user’s listening vector
to all country centroids.

We derive context from the self-reported country of a user. For our VAE model
with country context (i.e., model 1), a one-hot encoding of the 70 included
countries is used, whereas for VAE with cluster context (i.e., model 2),
context is determined by the user’s country membership in a cluster (see Table
1), resulting in a one-hot encoding of length 9. For the context models 3 and
4, we first calculate the cluster centroids, i.e., each track’s listening
events of all users belonging to a cluster are summed and then normalized by
the total amount of listening events across all tracks. Subsequently, for each
user, the Euclidean distances between the respective user’s normalized feature
vector and all cluster centroids are determined and used as context features
for the VAE with cluster distances (i.e., model 3). Country distances are
calculated accordingly, where each country is considered as its own cluster
(i.e., model 4). Taken together, $n\\_context$ is 70 in case of model 1 and
model 4, and 9 in case of model 2 and model 3.

Cluster |  Countries
---|---
0 |  ES, IT, IS, SI, PT
1 |  BE, NL, CH, SK, CZ, DE, AT, FI, PL
2 |  GB, EE, JP
3 |  AU, NZ, US, CA, PH
4 |  CL, CR, IL, UY
5 |  CO, MX, BG, GR
6 |  RO, EG, IR, TR, IN
7 |  BR, ID, VN, MY
8 |  LT, LV, UA, BY, RU, MD, KZ, GE
-1 |  AQ, FR, NO, ZA, IE, MK, AR, HR, RS, BA, HU, TW, DK, HK, SG, CN, KR, PE, TH, SE, PR, VE, GT
Table 1. Country clusters as determined by OPTICS with a minimum cluster size
of 3, based on the output of a t-SNE visualization (perplexity of 5) on PCA-
reduced country feature vectors (100 dimensions). Countries identified as too
noisy by OPTICS are represented as Cluster -1.

Our recommendation approach assumes that each user can be represented by a
latent $k$-dimensional multivariate Gaussian, which is sampled, weighted by
gates derived from context information, and transformed with a non-linear
function to reconstruct the initial track listening history (cf. Figure
LABEL:fig:vae_context_architecture). As mentioned before, our VAE model
without contextual features is based on the work of Liang et al. (2018). To
integrate context models, we extend the VAE by adding a gating mechanism to
feed in contextual information according to the four ways detailed above. In a
two-layer feed-forward neural network, the initial feature vector is encoded
first into an intermediate representation $enc1$ and then into a latent
$k$-dimensional multivariate Gaussian. The mean values $\boldsymbol{\mu}$ and
variance values $\boldsymbol{\sigma}$ are the outputs of the encoding network:

(1)
$\displaystyle\boldsymbol{enc}_{1}=\tanh\left(\boldsymbol{W}_{enc_{1}}\cdot\boldsymbol{t}\right)$
(2)
$\displaystyle\boldsymbol{\mu}=\tanh\left(\boldsymbol{W}_{enc_{\mu}}\cdot\boldsymbol{enc}_{1}\right)$
(3)
$\displaystyle\boldsymbol{\sigma}=\tanh\left(\boldsymbol{W}_{enc_{\sigma}}\cdot\boldsymbol{enc}_{1}\right)$

We use $tanh$ as a nonlinearity for all layers in the autoencoder. Based on
our experiments (see Section 5.2.1), we set the size of $W_{enc_{1}}$ to
$n\\_tracks$ $\times$ 1,200 and both $W_{enc_{\mu}}$ and $W_{end_{\sigma}}$ to
1,200 $\times$ 600\. This results in a length of 1,200 for $enc_{1}$ and 600
for the latent representation $z$. The user context, given by its input vector
$\boldsymbol{c}$ is transformed by a dense layer with sigmoid nonlinearity
into a context gate $c_{gate}$ of the same length as latent $z$. Next, the
gate is applied with component-wise multiplication to $z$:

(4)
$\displaystyle\boldsymbol{c}_{gate}=\sigma\left(\boldsymbol{W}_{context}\cdot\boldsymbol{c}\right)$
(5)
$\displaystyle\boldsymbol{\epsilon}\sim\mathcal{N}(\boldsymbol{0},\boldsymbol{1})$
(6)
$\displaystyle\boldsymbol{z}=\left(\boldsymbol{\mu}+\boldsymbol{\sigma}\odot\boldsymbol{\epsilon}\right)\odot\boldsymbol{c_{gate}}$

The weighted latent representation is then decoded back into the original
space by a network with mirroring size but different learned parameters of the
encoder:

(7)
$\displaystyle\boldsymbol{dec}_{1}=\tanh\left(\boldsymbol{W}_{dec_{1}}\cdot\boldsymbol{z}\right)$
(8)
$\displaystyle\boldsymbol{\hat{t}}=\tanh\left(\boldsymbol{W}_{dec_{2}}\cdot\boldsymbol{dec}_{1}\right)$

The detailed data flow and computation in each layer is visualized in Figure
LABEL:fig:vae_context_architecture. Based on the known track history of a
target user, the models generate a variational distribution $\hat{t}$. Top-$k$
track recommendations are then retrieved by ranking the mean values of this
distribution.

##  5\. Results and Discussion

In the following, we present and interpret the results of our approach to
identify country clusters and archetypes of music listening preferences
(Section 5.1) and of the music track recommendation experiments (Section 5.2).
We further connect the discussion to the initial research questions, which we
answer in the context of the obtained results.

###  5.1. Clustering of countries according to music listening preferences

We present the identified clusters and discuss the relationship of the
countries subsumed in each cluster beyond music preferences (Section 5.1.1),
for instance, in terms of geographic proximity, linguistic similarities, and
historical background. Furthermore, we discuss differences in user
characteristics such as the users’ gender, age, and their listening patterns
in terms of playcounts. In Section 5.1.2, we describe the characteristics of
the clusters with respect to music preferences, i.e, we detail the track
preferences that characterize the corresponding music archetypes.

####  5.1.1. Identified country clusters

Using the approach described in Section 4.2, we can identify nine country
clusters, which are presented in Table 1 and visualized in Figure 7. Cluster 0
contains Spain (ES), Portugal (PL), Italy (IT), Slovenia (SI), and Iceland
(IS). Cluster 1 includes as many as nine countries: Belgium (BE), The
Netherlands (NL), Austria (AT), Switzerland (CH), Germany (DE), Czech Republic
(CZ), Slovakia (SK), Poland (PL), and Finland (FI). Cluster 2 refers to the
United Kingdom (GB), Estonia (EE), and Japan (JP). Cluster 3 includes
Australia (AU), New Zealand (NZ), the United States (US), Canada (CA), and the
Philippines (PH). Cluster 4 refers to Chile (CL), Costa Rica (CR), Uruguay
(UY), and Israel (IL). Cluster 5 contains Colombia (CO), Mexico (MX), Bulgaria
(BG), and Greece (GR). Cluster 6 the following countries: Romania (RO), Egypt
(EG), Iran (IR), Turkey (TR), and India (IN). Cluster 7 is composed of Brazil
(BR), Indonesia (ID), Vietnam (VN), and Malaysia (MY). Cluster 8 encompasses
eight countries: Lithuania (LT), Latvia (LV), Ukraine (UA), Belarus (BY),
Russia (RU), Moldova (MD), Kazakhstan (KZ), and Georgia (GE).

Figure 7. Results of t-SNE (perplexity of 5) and OPTICS (minimum cluster size
of 3) on country feature vectors. The left part shows the full t-SNE output
space, the right part a zoomed version onto the major clusters.

Four of the countries in Cluster 0 are geographically tied together, sharing
national borders (i.e., Spain (ES), Portugal (PL), Italy (IT), and Slovenia
(SI)). Only Iceland (IS) is geographically dislocated. Furthermore, Spain
(ES), Portugal (PL), and Italy (IT) share their roots in Romanian language.
Moreover, there is a Slovene minority in Italy (IT), which may lead to partly
similar music preferences in Slovenia (SI) and Italy (IT).

Cluster 1 contains nine countries. Belgium (BE) and the Netherlands (NL) are
neighboring countries and share the official language spoken (note, Belgium
(BE) has two official languages). Austria (AT), Switzerland (CH), and Germany
(DE) share the German language (note, Switzerland (CH) has four official
languages). Czech Republic (CZ) and Slovakia (SK) are not only neighboring
countries, but actually formed one joint country until 1992. The languages
spoken in the Czech Republic (CZ), Slovakia (SK), and Poland (PL)—a
neighboring country to the former two—show strong linguistic similarities.
Altogether, we can see that Belgium (BE), the Netherlands (NL), Austria (AT),
Switzerland (CH), Germany (DE), Czech Republic (CZ), Slovakia (SK), and Poland
(PL) are geographically connected, sharing national borders (cf. Figure 8).
Only Finland (FI) is geographically disconnected from the other countries in
this cluster.

Figure 8. Countries in Cluster 1 on a map.

Cluster 2 delivers a highly surprising result because it contains three
countries that are geographically far away from each other without any
linguistic similarities or close historical connections: the United Kingdom
(GB), Estonia (EE), and Japan (JP). The United Kingdom (GB) and Estonia (EE)
are located at the Northwest and the Northeast of Europe—thus, at the opposite
borders of Europe; Japan (JP) is even almost 8,000 km farther east of Estonia
(EE). Although this cluster contains only three countries, with Japan (JP) and
the United Kingdom (GB), it embraces two of the largest music markets
worldwide (Statista Research Department, 2019). Interestingly, the United
Kingdom (GB) is not part of Cluster 3 that includes most English-speaking
countries. Considering the age distribution (Figure 9) in the identified
country clusters, we find that Cluster 2 shows the highest average age with a
relatively large span.141414Please note that observations concerning age
relate to our sample of Last.fm users. Furthermore, Cluster 2 shows by far the
highest average playcount per user for the countries in this cluster (Figure
10). This indicates that users in this cluster are characterized as being
‘power listeners’. As the combination of countries in this cluster seems
surprising, age and listening intensity may be the hidden—though
determining—aspects for the emergence of this cluster.

Figure 9. Age distribution of users in the identified country clusters. While
the oldest users can be found in Cluster 2, the youngest can be found in
Cluster 7. Figure 10. Distribution of users’ average playcount in the
identified country clusters. While the highest average playcount can be found
in Cluster 2, the lowest one can be found in Cluster 6.

The major connector of the countries in Cluster 3 is that they are all
English-speaking countries: Australia (AU), New Zealand (NZ), United States
(US), Canada (CA), and the Philippines (PH), where English is one of the two
official languages in both Canada (CA) and the Philippines (PH).

Cluster 4 comprises the countries Chile (CL), Uruguay (UY), Costa Rica (CR),
and Israel (IL). Both Chile (CL) and Uruguay (UY) are located in South America
and are connected by their language: Spanish. The official language in Costa
Rica (CR) is Spanish as well; located in Middle America, the geographic
distance to Chile (CL) and Uruguay (UY) is not far. Israel (IL), in contrast,
is a country in the Middle East and is, thus, geographically disconnected from
the other three countries in this cluster.

Cluster 5 contains two Latin-American countries as well as two countries in
Southeastern Europe. The Latin-American countries, i.e., Mexico (MX) and
Colombia (CO), are both Spanish-speaking countries. With Mexico (MX) located
in the Southern part of North America and Colombia (CO) being part of South
America, these are no neighboring countries, though. The two countries in
Southeastern Europe, i.e., Bulgaria (BG) and Greece (GR), share a border.
Thus, the cluster contains two country groups, which are geographically
spread.

The countries in Cluster 6 are geographically connected, centered around
countries being part of the Middle East—Turkey (TR), Iran (IR), and Egypt
(EG)—and flanked by Romania (RO), that has historical relations to the others
due to the Osman Empire, and India (IN), that is adjacent to the Middle East
and, thus, shows a geographical proximity to the other countries in this
cluster. Furthermore, all the countries in Cluster 6 are very diverse when it
comes to the various (minority) languages spoken, which may also be reflected
in music preferences. Considering the female/male ratio of users (Figure 11)
in the identified country clusters, we find that Cluster 6 shows the most
unevenly distributed ratio across the countries in this cluster. Despite the
wide span of female/male ratios in this cluster’s countries, Cluster 6 is the
cluster with the overall lowest female/male ratio compared to the other
clusters. With respect to age (Figure 9), this cluster comprises rather young
users in our sample of the Last.fm community (with the average age of users in
the Clusters 7 and 8 being even younger, though). Overall, with respect to age
and gender, Cluster 6 seems to have a differentiating profile compared to the
other clusters. Furthermore, Cluster 6 shows by far the lowest average
playcount per user (Figure 10). This low number could be the result of a
listening pattern that is shaped by cultural aspects, but could, for instance,
also be the consequence of limited access to the resources (e.g., broadband
Internet connection, streaming platforms operating in the respective
countries, licenses for music content). Considering those and similar aspects
is a fruitful path for future research.

Figure 11. Female/male ratio distribution of users in the identified country
clusters. We find that the female/male ratio is most unevenly distributed in
Cluster 6 and most evenly distributed in Cluster 7.

Cluster 7 covers three neighboring countries (with maritime borders) in the
Southeast of Asia—Indonesia (ID), Vietnam (VN), and Malaysia (MY)—and Brazil
(BR) in South America. The three countries in the Southeast of Asia have many
similarities, including common frames of reference in history, culture, and
religion; also their national languages are closely related. From a geographic
perspective, Brazil (BR) appears being disconnected from the other countries
in this cluster. The connection of Brazil (BR) with Indonesia (ID) and
Malaysia (MY) is that all three countries have formerly been Portuguese
colonies (Bada, 2018). Whether this historical connection is indeed also
conclusive for similar music preferences is subject to further research.
Referring back to Figure 9, where we plot the age distribution for the
identified country clusters, and Figure 11, where we plot the female/male
ratio, we see that Cluster 7 shows the lowest average age and is close to the
highest female/male ratio. Furthermore, the female/male ratio is very evenly
distributed in Cluster 7. We, thus, suspect that age and gender are the hidden
factors construing this cluster or, at least, accentuating it.

As can be seen from Figure 12, Cluster 8 comprises nine countries that are in
geographical proximity: the Baltic countries Lithuania (LT) and Latvia (LV),
the Russian Federation (RU), Ukraine (UA), Belarus (BY), Moldova (MD),
Kazakhstan (KZ), and Georgia (GA). Besides being characterized by the
geographic proximity, these countries share a history of having been part of
the Russian empire. Russian is a major (or influential) language in all of the
countries in this cluster (Central Intelligence Agency, 2019).

Figure 12. Countries in Cluster 8 on a map.

Overall, we note that the country clusters show different characteristics with
respect to age (Figure 9), gender (Figure 11), and average playcount per user
(Figure 10). With respect to age, we find especially large differences between
the Clusters 2 and 7: While the highest average age can be found in Cluster 2,
the lowest average age can be found in Cluster 7. The female/male ratio is
high in Cluster 7 and also evenly distributed. In contrast, the female/male
ratio is most unevenly distributed in Cluster 6 with a high span of ratios
across the countries in this cluster; and overall, the ratio is—in comparison
to the other clusters—very low. With respect to the average playcount per
user, it is also the Clusters 2 and 6 that show the largest differences: Among
the users in Cluster 2 there seems to be a high ratio of ‘power listeners’,
whereas the average playcount of users in Cluster 6 is low in comparison.
Overall, it can, thus, not be rejected that those and similar aspects may be
hidden factors that accentuate the differentiation between the clusters or may
even be indicative for the emergence of those clusters.

####  5.1.2. Characteristics of the identified clusters and music preference
archetypes

To address the question what characterizes the various clusters in terms of
music preferences, we use the approach described in Section 4.2 to identify
the most important tracks and genres for each cluster. Table 2 provides a list
of the 10 tracks with the highest playcounts per cluster (after the IDF-based
filtering explained in Section 4.2) and their genre annotations151515We
released the full list of the top 20 tracks (and corresponding artists) per
cluster and all microgenre annotations (for each track and artist) (cf.
“Accompanying Resources”).; for genre annotations, we rely on the user-
generated annotations retrieved from the Last.fm API and aligned with the
Spotify microgenres, as described in Section 4.2. These most important tracks
define the music preference archetypes corresponding to each cluster.

Cluster no. | Track title | Artist | Playcount within cluster | Track genres
---|---|---|---|---
0 | Mr. Brightside | The Killers | 4248 | rock, indie rock, alternative rock
| Uprising | Muse | 3955 | alternative rock, rock, progressive rock
| I Bet You Look Good on the Dancefloor | Arctic Monkeys | 3835 | indie rock, rock, alternative rock
| Fluorescent Adolescent | Arctic Monkeys | 3772 | indie rock, rock, alternative rock
| VCR | The xx | 3597 | electronic, indie rock, indie pop
| Reptilia | The Strokes | 3394 | indie rock, rock, alternative rock
| Mardy Bum | Arctic Monkeys | 3345 | indie rock, rock, alternative rock
| Hoppípolla | Sigur Rós | 3336 | post-rock, ambient, post-rock
| There Is a Light That Never Goes Out | The Smiths | 3289 | new wave, rock, brit-pop
| Teardrop | Massive Attack | 3260 | triphop, electronic, downtempo
1 | Set Fire to the Rain | Adele | 20460 | soul, pop, singer/songwriter
| Little Lion Man | Mumford & Sons | 20160 | folk, indie folk, banjo
| Otherside | Red Hot Chili Peppers | 19469 | rock, alternative rock, funk
| Radioactive | Imagine Dragons | 19338 | rock, indie rock, alternative rock
| VCR | The xx | 19220 | electronic, indie rock, indie pop
| Heart Skipped a Beat | The xx | 19004 | electronic, indie rock, rock
| Teardrop | Massive Attack | 18810 | triphop, electronic, downtempo
| Sail | AWOLNATION | 18728 | electronic, rock, indie rock
| The Pretender | Foo Fighters | 18636 | rock, alternative rock, grunge
| Cosmic Love | Florence + the Machine | 18486 | indie pop, rock, pop
2 | There Is a Light That Never Goes Out | The Smiths | 7479 | new wave, rock, brit-pop
| Mr. Brightside | The Killers | 7128 | rock, indie rock, alternative rock
| Little Lion Man | Mumford & Sons | 6979 | folk, indie folk, banjo
| R U Mine? | Arctic Monkeys | 6408 | indie rock, rock, alternative rock
| I Bet You Look Good on the Dancefloor | Arctic Monkeys | 6302 | indie rock, rock, alternative rock
| I Miss You | blink-182 | 6295 | rock, punk, pop-punk
| Teardrop | Massive Attack | 6187 | triphop, electronic, downtempo
| The Cave | Mumford & Sons | 6150 | folk, indie folk, banjo
| VCR | The xx | 6147 | electronic, indie rock, indie pop
| Harder Better Faster Stronger | Daft Punk | 6083 | electronic, house, electronica
3 | It Ain’t Cool To Be Crazy About You | George Strait | 19048 | country, traditional country,
| Electric Feel | MGMT | 18108 | electronic, electronica, indie pop
| Little Lion Man | Mumford & Sons | 17089 | folk, indie folk, banjo
| Time to Pretend | MGMT | 16802 | electronic, indietronica, electronica
| Flume | Bon Iver | 16032 | folk, singer/songwriter, indie folk
| In the Aeroplane Over the Sea | Neutral Milk Hotel | 15753 | indie rock, folk, lofi
| Midnight City | M83 | 15635 | electronic, electro-pop, electro
| 1901 | Phoenix | 15591 | indie pop, electronic, indie rock
| Such Great Heights | The Postal Service | 15481 | electronic, indie pop, electronica
| The Cave | Mumford & Sons | 15412 | folk, indie folk, banjo
4 | Mephisto | Dead Can Dance | 2468 | ambient, medieval, folk
| 3 Libras | A Perfect Circle | 1284 | alternative rock, progressive rock, rock
| Ariane | Nova | 1238 | –
| World’s End | Hatsune Miku & Megurine Luka | 1228 | –
| Mr. Brightside | The Killers | 1109 | rock, indie rock, alternative rock
| Las Fuerzas | Dënver | 1080 | –
| Jeremy | Pearl Jam | 1069 | grunge, rock, alternative rock
| Reckoner | Radiohead | 1064 | alternative rock, rock, experimental
| Them Bones | Alice in Chains | 1057 | grunge, rock, alternative rock
| Nude | Radiohead | 1050 | alternative rock, rock, electronic
5 | Häaden Two | Robert Fripp | 11616 | –
| The Smile | Phase | 7898 | alternative rock, progressive rock, art rock
| Ibidem | Phase | 7858 | alternative rock, art rock, rock
| Perdition | Phase | 7752 | rock, psychedelic rock, progressive rock
| Transcendence | Phase | 7690 | psychedelic rock, rock, alternative rock
| Hypoxia | Phase | 7614 | psychedelic rock, rock, alternative rock
| Static | Phase | 6988 | rock, progressive rock, space rock
| A Void | Phase | 6913 | rock, alternative rock, indie rock
| Static (Live) | Phase | 6877 | progressive rock, psychedelic rock, rock
| Evening On My Dark Hillside | Phase | 6793 | psychedelic rock, rock, alternative rock
6 | If I Could | Sophie Zelmani | 13420 | singer/songwriter, pop, folk
| I Can’t Change [New Song] | Sophie Zelmani | 13409 | –
| Without God | Katatonia | 8024 | doom metal, metal, death metal
| Day | Katatonia | 7947 | doom metal, metal, progressive metal
| Lady Of The Summer Night | Omega | 6787 | rock
| Sorrow | Pink Floyd | 6485 | progressive rock, rock, classic rock
| Equinoxe Part 5 | Jean Michel Jarre | 6457 | ambient, electronic rock,
| Gammapolis | Omega | 5958 | classic rock, progressive rock, space rock
| To Know You | Sophie Zelmani | 4783 | singer/songwriter, folk, pop
| To Know You (Alt. Version) | Sophie Zelmani | 4641 | –
7 | Set Fire to the Rain | Adele | 17247 | soul, pop, singer/songwriter
| Fluorescent Adolescent | Arctic Monkeys | 13007 | indie rock, rock, alternative rock
| Parade | Garbage | 11770 | rock, alternative rock, pop
| National Anthem | Lana Del Rey | 11602 | indie pop, pop, triphop
| Skyscraper | Demi Lovato | 11451 | pop, pop-rock, disney
| Come & Get It | Selena Gomez | 11387 | pop, electro-pop, dubstep
| Pumped Up Kicks | Foster the People | 11171 | indie pop, pop, indie rock
| Dark Paradise | Lana Del Rey | 11056 | pop, indie pop, chamber-pop
| Heart Attack | Demi Lovato | 10606 | pop, electro-pop, pop-rock
| You Only Live Once | The Strokes | 10501 | indie rock, rock, alternative rock
8 | Another Bottle Down | Asking Alexandria | 19779 | post-hardcore, metal-core, screamo
| Only You | Savage | 17657 | disco, pop, new wave
| …Meltdown | Enter Shikari | 16320 | post-hardcore, trance-core, electronic
| What You Want | Evanescence | 12345 | rock, alternative metal, gothic rock
| Gandhi Mate Gandhi | Enter Shikari | 12273 | post-hardcore, electronic, dubstep
| Dexter | Ricardo Villalobos | 11889 | minimal, minimal techno, electronic
| Paradise Circus | Massive Attack | 9922 | triphop, electronic, downtempo
| Teardrop | Massive Attack | 9891 | triphop, electronic, downtempo
| Kill Mercy Within | Korn | 9484 | numetal, electronic, dubstep
| Seven Nation Army | The White Stripes | 9380 | rock, alternative rock, indie rock

Table 2. The 10 most popular tracks per cluster. Playcount refers to the total
number of listening events by the users in each cluster.

The most popular tracks in Cluster 0 are mainly attributed to the microgenres
indie rock and alternative rock. Six tracks in the top 20 have indie rock as
the most associated microgenre, three alternative rock. Eight of 20 tracks
have both indie rock as well as alternative rock within their five most
associated microgenres. All of the 19 tracks among the top 20 that have
microgenres on track level (Si Te Quisieras Venir by the Los Planetas does not
have microgenres assigned on a track level), are associated with indie rock or
alternative rock; most of them with both. Only a few tracks in later positions
(thus, not in the top 10) deviate from these genres (e.g., Set Fire to the
Rain by Adele ranks on position 14 and is associated with the genres soul and
pop, Hurt by Johnny Cash is on position 16 and is mainly associated with
country and folk, or Get Lucky by Daft Punk feat. Pharrell Williams on the
position 20 that is associated with electronic). With 5 of the 20 most
frequently listened tracks in this cluster, the band Arctic Monkeys is
particularly dominant in that cluster.

While indie rock and alternative rock are represented in the most frequently
listened tracks in Cluster 0 as well as Cluster 1, the tracks in Cluster 1
differentiate insofar from those in Cluster 0 as there is a tendency that the
tracks include pop or electronic elements (e.g., VCR by The xx associated with
electronic and indie rock or Cosmic Love by Florence + the Machine). Four
tracks in the top 20 have indie pop as the most associated microgenre, 3
electronic. Ten tracks in the top 20 have indie rock as well as alternative
rock as tagged microgenres. For all tracks except Hurt by Johnny Cash and
Lonely Day by System of a Down, pop is one of the tagged microgenres.
Electronic is associated with 9 of the 20 tracks.

In Cluster 2, two tracks that are most associated with folk are among the most
popular tracks in the cluster (e.g., Little Lion Man or The Cave by Mumford &
Sons). Among the top 20, there are 4 tracks associated mostly with folk.
Tracks that are associated with electronic and pop (e.g., Judas by Lady Gaga)
and tracks associated with triphop and electronic (e.g., Teardrop by Massive
Attack) are also strongly represented. We recall Figure 9 showing that Cluster
2 has the highest average age in our sample of Last.fm users. The high average
age of users in Cluster 2 and the tendency to like folk music are in line with
previous research that found that folk music is more established among older
users compared to younger ones (Schedl and Ferwerda, 2017; Schedl and Bauer,
2017). Yet, the results in Schedl and Ferwerda (2017) suggest that the
preference for folk music is more prevalent for female than for male users;
this seems not to be fully in line with the characteristics of Cluster 7 at
first sight because the female/male ratio in Cluster 2 is not particularly
high (Figure 11). Delving deeper on the track characteristics, though, we
notice that previous works considered a rather coarse-grained taxonomy of
genres, whereas the work at hand considers microgenres. Table 2 shows that the
10 most popular tracks in Cluster 2 reflect indie rock (4 out of 10),
alternative rock (3 out of 10), and (indie) folk (2 out of 10). In previous
work (Schedl and Ferwerda, 2017), alternative (rock) was associated rather
with male users (typically with younger users, though). So the indie and
alternative element may suggest a rather male audience.

While the most listened song in Cluster 3 is associated with country (It Ain’t
Cool To Be Crazy About You by George Strait), this cluster shows a lot of
tracks that are tagged with folk among the most popular ones for that cluster;
4 of the top 20 have it as their most associated microgenre. The folk tracks
are either associated with folk and the singer/songwriter genre (e.g., Flume
or Holocene by Bon Iver) or are attributed to indie folk (e.g., In the
Aeroplane Over the Sea by Neutral Milk Hotel). Eleven tracks in the top 20 are
associated with electronic or electronica within the track’s five most tagged
microgenres.

The most popular tracks in Cluster 4 are predominantly associated with
progressive rock or alternative  rock (e.g., 3 Libras by A Perfect Circle).
Within the top 20 of this cluster, 10 tracks are associated with some form of
progressive rock and 2 with progressive metal, 14 with alternative rock, and 9
with some form of metal (i.e., progressive metal, alternative metal, doom
metal, or with the gernic term metal). An interesting deviation from the
dominance of the rock genre is the track World’s End by Hatsune Miku &
Megurine Luka, who is a vocaloid and j-pop artist. Indeed, all playcounts for
that track are generated by a single user 9 from Chile (CL); thus, this track
is not representative for Cluster 4. A further deviation is constituted by Por
la Ventana by Gepe associated with the genres folk and singer/songwriter,
which is listened to by more than one user.

The most popular tracks in Cluster 5 are mostly associated with the
psychedelic rock genre. Interestingly, 11 of the 20 most popular tracks are by
the band Phase. An exception from the strong psychedelic rock representation
in this cluster is the track Slow Me Down by Anneke van Giersbergen, a track
that is associated with the singer/songwriter genre, while the artist is
mainly associated with alternative rock and metal, but also pop-rock.

Cluster 6 is characterized by a dichotomy of genres among the most popular
tracks. On the one hand, there are tracks associated with singer/songwriter
and pop (e.g., If I Could and I Can’t Change by Sophie Zelmani). On the other
hand, there is a strong representation of doom metal with tracks such as
Without God and Day by Katatonia. Interestingly, both Sophie Zelmani as well
as Katatonia are present with several songs among the most popular tracks in
this cluster. Recalling the Figures 9, 11, and 10 that visualize the the user
characteristics for the eight clusters, uneven distribution with respect to
the female/male ratio and the generally low playcount per user (compared to
the other clusters), and the young age of its users may be characterizing
aspects for Cluster 6 that result in this heterogeneous picture with
singer/songwriter and pop tracks, on the one hand, and the strong
representation of doom metal, on the other. For instance, Schedl and Ferwerda
(2017) found pop being more popular among female than male users, while it is
the opposite for metal. Interestingly, the results of Schedl and Ferwerda
(2017) (considering a global sample, also relying on data from Last.fm)
suggest that the age group in which the users of Cluster 7 range, is the age
group that likes pop least of all analyzed age groups, and for liking of meta
this age group ranges in the middle field.

The only cluster that includes many popular tracks associated with the pop
genre is Cluster 7. Tracks include Skyscraper by Demi Lovato, Come & Get It by
Selena Gomez, and Dark Paradise by Lana Del Rey. Next to the generic tag pop
(19 occurrences), the most mentioned microgenres among the top 20 in this
cluster are poprock (16 occurrences) and indie pop (13 occurrences), followed
by britpop (9), electro pop (6), dance pop (6), dream pop (4), synth pop (3),
chamber pop (3), alternative pop (3), teen pop (2), art pop (2), power pop
(1), jangle pop (1), and k-pop (1). The high ratio of female users (Figure 11)
might be a cohesive characteristic in this cluster as already previous work
has shown that female users are more inclined to listen to pop music than male
users, in particular in the Last.fm community (Schedl and Ferwerda, 2017;
Schedl and Bauer, 2017).

Cluster 8 is characterized by the post-hardcore genre. Seven tracks in the top
20 in this cluster are tagged with post-hardhore, five of those have it as
their most tagged microgenre. Triphop (8 tracks), screamo (6 tracks), and
hardcore (6 tracks) are also well represented among the top 20 in this
cluster. Popular tracks include Another Bottle Down by Asking Alexandria,
…Meltdown by Enter Shikari, and Nineteen Fifty Eight by A Day to Remember. An
interesting deviation from this post-hardcore association are, for instance,
Dexter by Ricardo Villalobos (minimal techno) and Cookie Thumper! by Die
Antwoord (hip hop), which are also among the most popular tracks in this
cluster.

Summarizing the answer to RQ1, which we addressed here (To what extent can we
identify and interpret groups of countries that constitute music preference
archetypes, from behavioral traces of users’ music listening records?), we
find nine clusters of countries, with each of the clusters representing a
music preference archetype that reflects different nuances of music
preferences in terms of the Spotify microgenres. While some music preference
archetypes represent countries with geographical proximity (e.g., Cluster 6
and Cluster 8) and some archetypes share linguistic similarities (e.g.,
Cluster 3 and Cluster 8), others include interesting outliers (e.g, Iceland
(IS) in Cluster 0, Israel (IL) for Cluster 4, or Brazil (BR) in Cluster 7).

###  5.2. Music track recommendation using country context

In the following, we first detail the setup of the conducted evaluation
experiments for the music track recommendation task, including evaluation
protocol, baselines, and performance metrics (Section 5.2.1). Subsequently, we
report and discuss the obtained results ans answer the related research
questions (Section 5.2.2).

####  5.2.1. Experimental setup

After preselection and filtering (cf. Section 4.1), the dataset contains the
listening histories of 54,337 Last.fm users. To carry out the recommendation
experiments, we split the data into training, validation, and test sets. For
each of validation and test set, 5,000 users are randomly sampled. The
original VAE model (Liang et al., 2018) and our extended VAE architecture that
integrates the user context models described in Section 4.3 are trained on the
full listening events of the uses in the training set. For users in the
validation and test set, 80% of all listening events are randomly selected to
act as an input for the model, and the remaining 20% are used for evaluation.
The NDCG@100 metric (see below) on the validation set is used to select the
hyperparameters of our models.

Baselines: In addition to comparing our extended context-aware model to the
original VAE recommendation architecture (Liang et al., 2018), we also include
two baselines in the experiments, i.e., variants of most popular (MP) and
implicit matrix factorization (IMF). In the most popular (MP) models, a
popularity measure is calculated for each track based on its sum of listening
events across users in the training set. We implemented and evaluated three
flavors of MP: MP global computes the most popular tracks on a global scale
(independent of country); MP country considers only the top tracks in the
country of the target user; MP cluster considers only the top tracks within
the cluster the country of the target user belongs to. We then rank tracks
accordingly and use the ranking to produce recommendations, which are
evaluated on the 20% split of the test set (for each user). To make results
between the baseline and our proposed model comparable, we exclude tracks that
are part of a user’s known listening history, i.e., listening events from the
remaining 80%. As a second baseline, we adopt a collaborative filtering
approach using implicit matrix factorization (IMF) according to Koren et al.
(2009). We use the implementation provided by
Spotlight161616https://maciejkula.github.io/spotlight/factorization/implicit.html
with random negative sampling (50:50), 128 latent dimensions, and a pointwise
loss function.

Performance metrics: To quantify the accuracy of the recommendations, we use
the following metrics (similar to Liang et al., 2018; Schedl et al., 2018;
Aiolli, 2013), which we report averaged over all users (in the test set).
Thus, for each user in the test set, we generate recommendations using the
data in the training set and compare the recommended tracks with the actually
listened tracks of the user present in the test set in order to calculate the
performance metrics. Note that we use the definitions common in recommender
systems research, which are partly different from the ones traditionally used
in information retrieval.

Precision@K for user $u$:

(9) $\displaystyle P@K(u)=\frac{1}{K}\sum_{i=1}^{K}{rel(i)},$

where $K$ is the number of recommended items and $rel(i)$ is an indicator
function signaling whether the recommended track at rank $i$ is relevant to
$u$ or not. This means that $rel(i)=1$ if the recommended track at rank $i$
can be found in the test set; $rel(i)=0$ if not.

Recall@K for user $u$:

(10) $\displaystyle R@K(u)=\frac{1}{\min(K,N_{u})}\sum_{i=1}^{K}{rel(i)}$

where $N_{u}$ is the number of items in the test set that are relevant to $u$,
$K$ is the size of the recommendation list, and $rel(i)$ is the same indicator
function as used for Precision@K. When comparing Precision@K and Recall@K,
Precision@K can be seen as a measure of the usefulness of recommendations and
Recall@K as a measure of the completeness of recommendations.

Normalized discounted cumulative gain@K:

(11) $\displaystyle NDCG@K(u)=\frac{DCG@K(u)}{IDCG@K(u)}$

where $IDCG@K(u)$ is the ideal $DCG@K$ for user $u$, achieved when all items
relevant to $u$ are ranked at the top, and $DCG@K(u)$ is the discounted
cumulative gain at position $k$ for user $u$. It is given by:

(12) $\displaystyle
DCG@K(u)=\sum_{i=1}^{K}\frac{rel(i)}{\log_{2}\left(i+1\right)}$

where $rel(i)$ is the same indicator function as used for Precision@K and
Recall@K. In contrast to those two performance metrics, NDCG@K is a ranking-
based metric, which also takes the position of the recommended tracks into
account since higher-ranked items are given more weight.

We compute and report all metrics for $K=10$ and $K=100$, simulating users who
are just interested in a few top recommendations and users who inspect a large
part of the recommendation list, respectively.

####  5.2.2. Results and discussion

Table 3 shows the performance achieved on the test set, averaged over all
users in the test set. As a general observation, we see that the VAE-based
approaches outperform the baselines (MP and IMF) by a substantial extent. Of
the baselines, IMF performs superior to MP global while the other two variants
of MP (MP country and MP cluster) yield better results than IMF. The poor
performance of MP global is somewhat surprising since several studies (e.g.,
Tiwari et al., 2018; Lai et al., 2019; Vall et al., 2019) have shown that
recommendation approaches leveraging popularity information—e.g., always
suggesting the items that are most frequently consumed—often achieve highly
competitive accuracy values in offline experiments, despite the obvious fact
that such recommendations will likely not be perceived very useful by the
users. A likely reason is that we perform track recommendation while the
earlier mentioned works commonly adopt an artist recommendation setup. In an
artist recommendation scenario, it is very likely that a user has consumed
every highly popular artist at least once. This leads to a high performance of
a popularity-based approach. In the track recommendation scenario adopted in
the work at hand, the granularity of items (tracks vs. artists) is higher
and—in comparison to the artist recommendation scenario—it is not necessarily
the case that the most popular tracks have been consumed by most users at
least once. Overall, a popularity-based approach may work well for artist
recommendation but less so for the more fine-grained track recommendation.

Model | P@10 | P@100 | R@10 | R@100 | NDCG@10 | NDCG@100
---|---|---|---|---|---|---
MP global | 0.048 | 0.033 | 0.048 | 0.036 | 0.050 | 0.037
MP country | 0.203 | 0.156 | 0.203 | 0.157 | 0.209 | 0.166
MP cluster | 0.193 | 0.149 | 0.193 | 0.149 | 0.199 | 0.158
IMF | 0.080 | 0.072 | 0.080 | 0.064 | 0.081 | 0.071
VAE | 0.482 | 0.309 | 0.486 | 0.367 | 0.500 | 0.383
VAE country id (model 1) | 0.513 | 0.325 | 0.517 | 0.384 | 0.532 | 0.402
VAE cluster id (model 2) | 0.515 | 0.326 | 0.520 | 0.385 | 0.537 | 0.404
VAE cluster dist (model 3) | 0.513 | 0.325 | 0.518 | 0.384 | 0.534 | 0.403
VAE country dist (model 4) | 0.516 | 0.325 | 0.521 | 0.383 | 0.535 | 0.403
VAE sampling | 0.224 | 0.099 | 0.239 | 0.255 | 0.252 | 0.223
VAE sampling country id (m. 1) | 0.230 | 0.102 | 0.245 | 0.259 | 0.259 | 0.227
VAE sampling cluster id (m. 2) | 0.231 | 0.101 | 0.246 | 0.259 | 0.261 | 0.227
VAE sampl. cluster dist (m. 3) | 0.232 | 0.102 | 0.245 | 0.258 | 0.246 | 0.258
VAE sampl. country dist (m. 4) | 0.225 | 0.100 | 0.239 | 0.255 | 0.255 | 0.223
Table 3.  Results with respect to Precision@K, Recall@K, and NDCG@K metrics.
For all metrics, pairwise comparison using a Wilcoxon signed-rank test shows
significant improvements from MP global to IMF to VAE to all VAE models with
context (models 1–4); there are no significant differences between the 4 VAE
models that use context, though. The five rows at the bottom (“VAE sampling
…”) show results for another set of experiments in which we randomly sampled
(three times) exactly 122,442 tracks from about 1 million tracks instead of
computing performance measures on the top 122,442 tracks of the whole
collection as done in the main experiment.

On the other hand, we also note that the other two variants (MP country and MP
country) achieve much better results than MP global, even outperforming the
IMF approach. This might be explained by the more narrow but better user-
tailored consideration of the country-specific mainstream (cf. Bauer and
Schedl (2019)), which is reflected in the computation of most popular tracks
in the MP country and MP cluster models.

Comparing the proposed context-aware extensions of the VAE recommendation
architecture to the original VAE (Liang et al., 2018), we observe a clear
improvement of all metrics when integrating the user context models. This
improvement is achieved irrespective of the actual user model we adopt (models
1–4). Precision@10 increases by 3.4 percentage points (7.1%) from VAE to the
best performing VAE context model (model 4) that leverages the distances
between users and country centroids. Likewise, Precision@100 increases by 1.7
percentage points (5.5%). Recall@10 and Recall@100 improve, respectively, by a
maximum of 3.5 percentage points (7.2%), realized by model 4, and by 1.8
percentage points (4.9%), realized by model 2. In terms of NDCG, the largest
gains are realized by VAE context model 2 that incorporates cluster ids.
NDCG@10 improves by 3.7 percentage points (7.4%) compared to VAE; NDCG@100
increases by 2.1 percentage points (5.5%).

We investigate statistical significance of the results as follows. For all
used metrics (i.e., P@10, P@100, R@10, R@100, NDCG@10, NDCG@100), data is non-
normally distributed (Kolmogorov-Smirnov test, $p\leq 0.001$). Accordingly, we
use the Friedman test (Friedman, 1937) to test the models’ performances for
differences. For each metric, the models differ at a significance level of
$p\leq 0.001$. In pairwise comparisons using a Wilcoxon signed-rank test
(Wilcoxon, 1945), for each metric, the tests indicate that VAE outperforms
each of the baselines (i.e, MP an IMF) at a significance level of $p\leq
0.05$. Furthermore, we perform a pairwise comparison, again using Wilcoxon’s
signed-rank test, for each metric and each pair of pure VAE and one of the
models integrating context information (i.e., models 1–4). For each metric and
each of the models 1–4, the models 1–4 outperform the pure VAE (without
context integration) at a significance level of $p\leq 0.05$. Yet, the
Friedman test did not indicate any significant differences of the models 1–4
for any of the metrics.

Returning to the original research questions, we answer RQ2 (Which are
effective ways to model the users’ geographic background as a contextual
factor for music recommendation?) by pointing to the fact that all four user
models proposed are effective to significantly improve recommendation quality
in terms of precision, recall, and NDCG measures. We note, however, that
performance differences between the four user context models are largely
negligible. In summary, leveraging country information for music track
recommendation (either as country or cluster identifier, or as distances
between the target user and each cluster’s centroid) is beneficial compared to
not including any country information.

As for RQ3 (How can we extend a state-of-the-art recommendation algorithm to
include user context information, in particular, our geo-aware user models?),
we proposed an extension of a state-of-the-art recommender based on a VAE
architecture (Liang et al., 2018), i.e., we devised a multi-layer generative
model in which contextual features can influence recommendations through a
gating mechanism.

To investigate the generalizability of our findings to a dataset with
different characteristics, we perform an additional experiment as follows. We
estimate performance on a more diverse dataset in terms of track popularity
than the one that considers only the top 122,442 tracks. More precisely, we
create a second dataset by first considering all tracks that have been
listened to as least 100 (instead of 1,000) times, yielding 1,012,961 unique
tracks. We then randomly sample, three times, exactly the same amount of
tracks (122,442) as used in our main experiment, and we evaluate the VAE
approaches on each randomly sampled subset, averaging performance measures
across the three runs.171717Please note that computational limitations
prevented us from running experiments on all 1,012,961 tracks, even more so on
the entire LFM-1b dataset. Results can be found in the five last rows of Table
3 (models named “VAE sampling …”). While we observe an obvious decrease in
performance when considering items further down the popularity scale, results
are still in line with the findings obtained on the main dataset. In
particular, our extended VAE models (models 1–4) still outperform the original
VAE architecture, with respect to all performance metrics.

##  6\. Conclusions, Limitations, and Future Work

In summary, we approached the task of identifying country clusters and
corresponding archetypes of music consumption preferences based on behavioral
data of music listening that originates from Last.fm users. Together with the
users’ self-disclosed country information, we used the listening data (369
million listening events created by 54 thousand Last.fm users) as an input to
unsupervised learning techniques (t-SNE and OPTICS), allowing us to identify
nine archetypal country clusters. We discussed these clusters in detail with
respect to their corresponding users’ music preferences on the track level and
the linguistic, historical, and cultural backgrounds of the countries in each
cluster. Additionally, we considered the distribution of age, gender, and
average playcount per user as aspects in our analysis.

Furthermore, we proposed a context-aware music recommendation approach
operating on the music track level, which integrates different user models
that are based on the user’s country or country cluster. To this end, we
extended a variational autoencoder (VAE) architecture by a gating mechanism to
add contextual user features. We considered four user models, either encoding
the target user’s country information (model 1) or cluster information (model
2) directly, or as a feature vector containing the distances between the
target user and all cluster centroids (model 3) or all individual country
centroids (model 4). In evaluation experiments, using precision, recall, and
NDCG as performance metrics, we showed that all VAE architectures outperformed
a popularity-based recommender and implicit matrix factorization, which served
as our baselines. Results further revealed superior performance of all VAE
variants that include context information vis-à-vis VAE without context
information, regardless of how country information is encoded in the user
model.

Yet, this work has potential limitations with respect to the underlying
dataset, which we discuss in the following. There are social patterns that
define how and why people access music (López-Sintas et al., 2014). A dataset
containing logs of the interactions with an online platform can, thus, only
capture those listening events of people using any form of online music
platform. According to López-Sintas et al. (2014), music access patterns are
structured by an individual’s social position (indicated by education) and
life stage (indicated by age). A bias with respect to the users’ social
background can therefore be expected for our dataset. For instance, the
dataset has a strong community bias towards users in the United States (US),
while other countries are less represented. Furthermore, user information is
self-reported by the users, which may be prone to errors and may not
necessarily reflect the truth. For instance, some users report as their
country Antarctica (AQ) or a birth year of 1900, which both do not seem overly
plausible—especially in combination (also see Figure 1 in Schedl, 2017).
Moreover, some users show very high playcounts for single tracks, which are
not popular among other users. This also affects six of the tracks presented
in our discussion of the music preference archetypes. For instance, World’s
End by Hatsune Miku & Megurine Luka has a playcount of 1,228 generated by a
single unique user. Similarly, One Thing’ by Runrig and Resemnare by Valeriu
Sterian both have exactly one unique listener, who generated a playcount of
4,000 and 3,591, respectively. The track Ariane by Nova has 3 unique users; I
Can’t Change [New Song] and To Know You (Alt. Version)—both by Sophie
Zelmani—have 5 unique users each, whereof almost all playcounts were generated
by only one single user. For both songs, this is the same user. Notably, also
the preferences of the Last.fm users in our dataset towards certain genres
differ from the genre preferences of the population at large. For instance, we
found that rap and R&B as well as classical music is substantially
underrepresented in Last.fm listening data (Schedl and Tkalcic, 2014), which
we use in the present study.  To some extent, these limitations related to the
dataset could be alleviated in the future by performing further data cleansing
and preprocessing steps, e.g., threshold-based filtering of exorbitant
playcounts by a minority of listeners.

Another limitation of the work concerns a characteristic of t-SNE, which is
that the cost function t-SNE uses is non-convex. This, in turn, may result in
a different embedding of data points in the low-dimensional output space when
the t-SNE algorithm is run on different software or hardware
configurations.181818Note that our results are stable for a given machine,
software configuration, and parameter setting since we fixed the seed of the
random number generator. Running the code on other configurations, however,
may result in a slightly different visualization and clustering. Please note
that this does not only concern the present work, but potentially the entire
(large) body of research that employs t-SNE for visualization. It is, however,
an aspect that is barely discussed. We address this issue in the current work
by providing exact details on our implementation and used software, and by
releasing to the public the source code, parameter configurations, and dataset
used in our experiments (cf. “Accompanying Resources” below).

In this work, we used simple mechanisms to integrate country information as
context factors into a VAE architecture. While they worked out well, i.e.,
outperformed a non-context-aware VAE, we expect even better performance with
other user models, whose creation will be part of future research. For
instance, we contemplate using probabilistic models to describe the likelihood
of each user to belong to each cluster (or country), e.g., via Gaussian
mixture models. Given the actual country of a user, we could then analyze in
more detail users whose stated country is not the country with highest
probability. Such a framework could also be used to diversify recommendations
according to a user-selected country, fulfilling user intents such as “I want
music of my preferred genre, but listened to by Brazilians”.

Furthermore, it would be worthwhile to compare the clustering and
recommendation results we achieved here on the track level to results achieved
when modeling music preferences on the artist level, keeping all other
methodological details the same. In particular, since previous studies have
predominantly shown that popularity-based music recommendation systems perform
well when recommending artists, such a comparison could be enlightening.

Finally, we aim at delving into the possible cultural, historical, or socio-
economic reasons that may underlie the differences in music preferences
between the identified archetypes. To this end, we will consider theories and
insights from cultural sciences, history, sociology, and economics, and
connect our music preference archetypes to these theories. Another promising
path for further analysis of the country clusters is to consider dimensions
rooted in the music market or the music content itself, including
considerations such as as local demand, production of music styles, reception
of music styles, diffusion, etc., as well as dimensions related to the users’
listening habits.

## Accompanying Resources

To foster reproducibility, we release the code and data used in this work to
the public. The code can be found on
https://gitlab.cp.jku.at/markus/fiai2020_country_clusters; the dataset (Schedl
et al., 2020) is available from
https://zenodo.org/record/3907362#.XvRq1CgzZPY. In our experiments, we used
the following setup: Windows 10 Home, Build 18362; Python 3.7.0; T-SNE and
OPTICS implementations of scikit-learn 0.21.3. The remaining, system-
independent, configuration details can be found in the code.

## Funding

This research is supported by the Austrian Science Fund (FWF): V579 and the
Know-Center GmbH (FFG COMET funding).

## Acknowledgments

The authors would like to thank Peter Müllner from the Know-Center GmbH for
providing the IDF calculations of the music tracks.

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