TY - GEN
T1 - Baby shark to Barracuda
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
N1 - Publisher Copyright:
© 2021 Owner/Author.
PY - 2021/9/13
Y1 - 2021/9/13
N2 - 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.
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.
KW - Children
KW - Music recommendation
KW - Music traits
KW - Preferences
UR - https://www.scopus.com/pages/publications/85115639451
U2 - 10.1145/3460231.3478856
DO - 10.1145/3460231.3478856
M3 - Conference contribution
AN - SCOPUS:85115639451
T3 - RecSys 2021 - 15th ACM Conference on Recommender Systems
SP - 639
EP - 644
BT - RecSys 2021 - 15th ACM Conference on Recommender Systems
Y2 - 27 September 2021 through 1 October 2021
ER -