Traces of Globalizationin Online Music Consumption Patterns
and Results of Recommendation Algorithms

HCAIMMS
GROUP

Institute ofComputational
Perception

Oleg Lesota1,2, Emilia Parada-Cabaleiro1,2, Stefan Brandl1,
Elizabeth Lex3, Navid Rekabsaz1,2, Markus Schedl1,2
1Johannes Kepler University Linz, 2Linz Institute of Technology, 3Graz University of Technology (Austria)

Motivation

DatasetA

Culturalexchange
2.0
B

User listening activity on Last.fm in 2018-2019 (sample of LFM-2b dataset)
Artist country information crawled from MusicBrainz.org
We concentrate on 20 countries with at least 100 users and 1000 tracksAmong them US is the most represented with 1,763 users and 252,370 tracks
Least represented is Turkey with 115 users and 1,478 tracks

•••
•
•

TotalAB

Last.fm Presence•C

Right: distribution of attention users
from different countries pay to US
made and their domestic music.

C•

CA!, IT", NL# and others show
above average consumption of US
music and below average ofdomestic
music,
possibly
suppressed by global trends•

Fewer countries such as GB$, BR%,SE&, pay above average attention
to both local and US music, hinting
on comfortable coexistence of thetwo•

Some countries are influenced by
US music to a smaller degree, e.g.,PL', TR(, FI)

Research questions
RQ 1: How prominent is the aspect of US “cultural imperialism” in the sphere of
online music consumption? Is its influence uniform across countries?
RQ 2: How significant is domestic music consumption in different countries? Are
there any signs of “glocalization”?
RQ 3: Do music recommender systems influence users’ inclination towards musiccoming from certain countries?

•••

Method• u – user id, t – music track idUserCountryu1!

t1 !, t2 !, t3 "u2!

t2 !, t5 #Each user comes from country cu•

Each track is produced by artist from ct•Listening event (LE) – tuple <u, t>"utt#,"
354
signifying that user u played track t
In the toy example on the right user u3 trigered two LEs: <u3 , t5> and <u3 , t4>
We denote all LEs produced by users from cu with tracks from ct as 𝑰𝒄𝒖, 𝒄𝒕
We denote all LEs produced by users from cu as 𝑰𝑼𝒄𝒖
We denote all LEs with tracks from ct as 𝑰𝑨𝒄𝒕Right:
proportionof“domesticattention” received by artists fromdifferent countries.
•GB$, SE& adapt and contribute to
global trends; their music receives
notable attention from without•

Most attention to BR% music comesfrom within; heavy adaptation oftrends•FI) is less affected by US trends, yet its
music is appreciated in other countries

RQ 3: Recommendation experiment|𝑰cu , 𝑪𝒕 |𝑨𝑻𝑻cu , 𝑪𝒕 =|𝑰𝑼cu |
JP:
0.0

0.2

0.4

0.6

0.8!
*Music fromCountAttention!

10.2! (domestic)

30.6

other10.2

1.0

RQ 2: Attention from domestic publicWhich proportion of attention
to music from country ct = +comes from “domestic” public?

|𝑰ct , 𝑪𝒕 |𝑷𝑫𝑷ct =|𝑰𝑨ct |1.0Music from0.5
0.0!

Users fromCount Proportion" (domestic)

10.5

other

1

0.5

ItemKNN• On average recommends thesame proportion of US music asconsumed by the users

USRQ 3: Recommendation experiment
Do recommenders suggest more or less local music than users actually consume?
• Assuming users consume all top 10 tracks recommended to them we compare thedistribution of US / local music recommended to actual user consumption, as
calculated for RQ 1
JP:0.0••

0.2

0.4

0.6

0.8

"
1.0

Consumed US " Music
Consumed JP ! MusicRecommended US " Music
Recommended JP ! Music

We consider two recommendation algorithms: ItemKNN, NeuMF
Three samples of 100k items are taken as training sets, average results reported!
"#$%&'()
*+,.
/
01
2
34!
"
#$%&'()
*+,.
/01
2
34

RQ 1: Domestic vs US-produced musicHow much attention do listeners fromcountry cu = * pay to music produced in Users fromthe US and in their own country?

!
"#$%&'()
*+,.
/
0
1
2
3
4

RQ 2: Glocalization

Played Tracks•••••

12,875Interactions
14,640,001

RQ 1: Cultural imperialism

With the emergence of online streaming platforms music consumption becomes
centralizedMusic appreciated by users of more represented cultures may overshadow music
preferred by users of less represented cultures
In 2018-2019 about 40% of music tracks scrobbled on Last.fm were produced byartists from the United States•

UsersResults

Music Artist Country•

Tracks637,236

NeuMF• Recommends bigger proportion ofUS music• Suppresses domestic musicConclusions•••

RQ 1: US produced music holds strong positions among Last.fm users, however
its influence is not uniform across countries
RQ 2: We observe different patterns of Glocalization - local culture confronting
or adapting global trends
RQ 3: Recommender systems may influence the mode of cultural exchange
between countries. The influence is algorithm-dependent

This research was funded in part, by the Austrian Science Fund (FWF): P33526 and DFH-23; as well as by the State of Upper Austria and the Federal Ministry of Education, Science, and Research, through grants LIT-2020-9-SEE-113 and LIT-2021-YOU-215.

oleg.lesota@jku.at
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