Beyond the black box in music streaming: the impact of recommendation systems upon artists

被引:14
|
作者
O'Dair, Marcus [1 ]
Fry, Andrew [1 ]
机构
[1] Univ Arts London, Knowledge Exchange & Enterprise, London, England
关键词
Digital media; information society; media economics; media industries; popular music; Spotify; recommendation system; personalised playlist;
D O I
10.1080/15405702.2019.1627548
中图分类号
G2 [信息与知识传播];
学科分类号
05 ; 0503 ;
摘要
As algorithms have emerged as a key site of power in contemporary culture and society, they have been scrutinised by a number of media scholars, variously focusing on their opacity, bias and social implications. There has also been important work calling for a shift of attention away from the algorithms themselves to the actors that control them: the fundamental questions we should be asking of algorithms, after all, concern more than the specifics of code. This paper applies the arguments developed by Gillespie and Bucher to the algorithms utilised by music streaming services - the powerful but opaque curatorial systems that suggest songs to users. Although there has been important work on algorithms in the context of music streaming, this focus on music streaming remains relatively unusual. Even in the context of music streaming algorithms, our approach is also novel, in that we focus not on the possible effects upon users of music streaming platforms - that is, music fans - but, rather, on the possible effects on music creators. What, then, might be the effects upon songwriters and artists of the increasing prevalence of recommendation systems in music streaming?
引用
收藏
页码:65 / 77
页数:13
相关论文
共 17 条
  • [1] SPOTIFY TEARDOWN: INSIDE THE BLACK BOX OF STREAMING MUSIC
    James, Robin
    AMERICAN BOOK REVIEW, 2019, 40 (06) : 13 - 14
  • [2] Spotify Teardown: Inside the Black Box of Streaming Music
    Deaville, James
    JOURNAL OF POPULAR MUSIC STUDIES, 2020, 32 (03) : 153 - 155
  • [3] Spotify Teardown: Inside the Black Box of Streaming Music
    Seaver, Nick
    INFORMATION & CULTURE, 2019, 54 (03): : 396 - 398
  • [4] Spotify teardown: inside the black box of streaming music
    Maceviciute, Elena
    INFORMATION RESEARCH-AN INTERNATIONAL ELECTRONIC JOURNAL, 2019, 24 (01):
  • [5] Spotify Teardown: Inside the Black Box of Streaming Music.
    Jones, Ellis
    CONVERGENCE-THE INTERNATIONAL JOURNAL OF RESEARCH INTO NEW MEDIA TECHNOLOGIES, 2019, 25 (04): : 782 - 783
  • [6] Listening to Live Music: Life Beyond Music Recommendation Systems
    Kostek, Bozena
    2018 JOINT CONFERENCE - ACOUSTICS, 2018, : 135 - 139
  • [7] Beyond the Big Five personality traits for music recommendation systems
    Mariusz Kleć
    Alicja Wieczorkowska
    Krzysztof Szklanny
    Włodzimierz Strus
    EURASIP Journal on Audio, Speech, and Music Processing, 2023
  • [8] Beyond the Big Five personality traits for music recommendation systems
    Klec, Mariusz
    Wieczorkowska, Alicja
    Szklanny, Krzysztof
    Strus, Wlodzimierz
    EURASIP JOURNAL ON AUDIO SPEECH AND MUSIC PROCESSING, 2023, 2023 (01)
  • [9] Beyond the black box: Digital media players as interoperable systems
    Baumann, Chris
    JOURNAL OF POPULAR TELEVISION, 2019, 7 (02) : 201 - 215
  • [10] A novel shilling attack on black-box recommendation systems for multiple targets
    Shuangyu Liu
    Siyang Yu
    Huan Li
    Zhibang Yang
    Mingxing Duan
    Xiangke Liao
    Neural Computing and Applications, 2025, 37 (5) : 3399 - 3417