Baby shark to Barracuda: Analyzing children's music listening behavior

Lawrence Spear; Ashlee Milton; Garrett Allen; Amifa Raj; Michael Green; Michael D. Ekstrand; Maria Soledad Pera

doi:10.1145/3460231.3478856

Baby shark to Barracuda: Analyzing children's music listening behavior

Lawrence Spear, Ashlee Milton, Garrett Allen, Amifa Raj, Michael Green, Michael D. Ekstrand, Maria Soledad Pera

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

2 Citations (Scopus)

Abstract

Music is an important part of childhood development, with online music listening platforms being a significant channel by which children consume music. Children's offline music listening behavior has been heavily researched, yet relatively few studies explore how their behavior manifests online. In this paper, we use data from LastFM 1 Billion and the Spotify API to explore online music listening behavior of children, ages 6-17, using education levels as lenses for our analysis. Understanding the music listening behavior of children can be used to inform the future design of recommender systems.

Original language	English
Title of host publication	RecSys 2021 - 15th ACM Conference on Recommender Systems
Publisher	Association for Computing Machinery (ACM)
Pages	639-644
Number of pages	6
ISBN (Electronic)	9781450384582
DOIs	https://doi.org/10.1145/3460231.3478856
Publication status	Published - 13 Sept 2021
Externally published	Yes
Event	15th ACM Conference on Recommender Systems, RecSys 2021 - Virtual, Online, Netherlands Duration: 27 Sept 2021 → 1 Oct 2021

Conference

Conference	15th ACM Conference on Recommender Systems, RecSys 2021
Country/Territory	Netherlands
City	Virtual, Online
Period	27/09/21 → 1/10/21

Keywords

Children
Music recommendation
Music traits
Preferences

Access to Document

10.1145/3460231.3478856

Cite this

@inproceedings{a946bfe0a1474084847f94b91e167ea2,

title = "Baby shark to Barracuda: Analyzing children's music listening behavior",

abstract = "Music is an important part of childhood development, with online music listening platforms being a significant channel by which children consume music. Children's offline music listening behavior has been heavily researched, yet relatively few studies explore how their behavior manifests online. In this paper, we use data from LastFM 1 Billion and the Spotify API to explore online music listening behavior of children, ages 6-17, using education levels as lenses for our analysis. Understanding the music listening behavior of children can be used to inform the future design of recommender systems. ",

keywords = "Children, Music recommendation, Music traits, Preferences",

author = "Lawrence Spear and Ashlee Milton and Garrett Allen and Amifa Raj and Michael Green and Ekstrand, {Michael D.} and Pera, {Maria Soledad}",

year = "2021",

month = sep,

day = "13",

doi = "10.1145/3460231.3478856",

language = "English",

pages = "639--644",

booktitle = "RecSys 2021 - 15th ACM Conference on Recommender Systems",

publisher = "Association for Computing Machinery (ACM)",

address = "United States",

note = "15th ACM Conference on Recommender Systems, RecSys 2021 ; Conference date: 27-09-2021 Through 01-10-2021",

}

Spear, L, Milton, A, Allen, G, Raj, A, Green, M, Ekstrand, MD & Pera, MS 2021, Baby shark to Barracuda: Analyzing children's music listening behavior. in RecSys 2021 - 15th ACM Conference on Recommender Systems. Association for Computing Machinery (ACM), pp. 639-644, 15th ACM Conference on Recommender Systems, RecSys 2021, Virtual, Online, Netherlands, 27/09/21. https://doi.org/10.1145/3460231.3478856

Baby shark to Barracuda: Analyzing children's music listening behavior. / Spear, Lawrence; Milton, Ashlee; Allen, Garrett et al.
RecSys 2021 - 15th ACM Conference on Recommender Systems. Association for Computing Machinery (ACM), 2021. p. 639-644.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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T2 - 15th ACM Conference on Recommender Systems, RecSys 2021

AU - Spear, Lawrence

AU - Milton, Ashlee

AU - Allen, Garrett

AU - Raj, Amifa

AU - Green, Michael

AU - Ekstrand, Michael D.

AU - Pera, Maria Soledad

PY - 2021/9/13

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AB - Music is an important part of childhood development, with online music listening platforms being a significant channel by which children consume music. Children's offline music listening behavior has been heavily researched, yet relatively few studies explore how their behavior manifests online. In this paper, we use data from LastFM 1 Billion and the Spotify API to explore online music listening behavior of children, ages 6-17, using education levels as lenses for our analysis. Understanding the music listening behavior of children can be used to inform the future design of recommender systems.

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KW - Music recommendation

KW - Music traits

KW - Preferences

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Y2 - 27 September 2021 through 1 October 2021

ER -

Baby shark to Barracuda: Analyzing children's music listening behavior

Abstract

Conference

Keywords

Access to Document

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