Shaping the Stream: Techniques and Troubles of Algorithmic Recommendation

Goldschmitt, K. E.; Seaver, Nick

doi:10.1017/9781316676639.006

Cited by 73 publications

(10 citation statements)

References 190 publications

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“…The related artists page. Digital platforms play a major role in reproducing inequalities, which have already existed in the offline music world through several mechanisms, partly through their recommendation systems (Bauer, 2019;Celma, 2010;Goldschmitt and Seaver, 2019). For this very reason, we concentrated on one of the recommendation types of Spotify.…”

Section: Methodsmentioning

confidence: 99%

“…Instead of owning recorded music in material formats, immaterial music formats are not owned but accessed by listeners through the cloud (Burkart, 2014). Streaming services offer music as a service, as a "utility" (Goldschmitt and Seaver, 2019) rather than selling individual music tracks. The platform ecosystem reproduces the inequalities and center-periphery dynamics of the cultural industries through their central position in the economy, the content distribution and consumption patterns they facilitate, and recommendation systems that manage the content flow and the user-interface interaction.…”

Section: Digital Music Platforms and Recommendation Systems As Spaces Of Inequality Reproductionmentioning

confidence: 99%

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“Splendid Isolation”: The reproduction of music industry inequalities in Spotify’s recommendation system

Tófalvy

Koltai

2021

New Media & Society

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In this article, we argue that offline inequalities, such as core–periphery relations of the music industry, are reproduced by streaming platforms. First, we offer an overview of the reproduction of inequalities and core–periphery dynamics in the music industry. Then we illustrate this through a small-scale network analysis case study of Hungarian metal bands’ connections on Spotify. We show that the primary determinant of a given band’s international connectedness in Spotify’s algorithmic ecosystem is their international label connections. Bands on international labels have more reciprocal international connections and are more likely to be recommended based on actual genre similarity. However, bands signed with local labels or self-published tend to have domestic connections and to be paired with other artists by Spotify’s recommendation system according to their country of origin.

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Section: Methodsmentioning

confidence: 99%

Section: Digital Music Platforms and Recommendation Systems As Spaces Of Inequality Reproductionmentioning

confidence: 99%

“Splendid Isolation”: The reproduction of music industry inequalities in Spotify’s recommendation system

Tófalvy

Koltai

2021

New Media & Society

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“…What has a listener liked in the past? A master algorithm orchestrates the sub-algorithms’ outputs together into an ‘ensemble’ (Goldschmitt and Seaver, nd) that makes a simple decision: What song should be played next?…”

Section: Hookedmentioning

confidence: 99%

Captivating algorithms: Recommender systems as traps

Seaver

2018

Journal of Material Culture

Self Cite

280

119

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Algorithmic recommender systems are a ubiquitous feature of contemporary cultural life online, suggesting music, movies, and other materials to their users. This article, drawing on fieldwork with developers of recommender systems in the US, describes a tendency among these systems’ makers to describe their purpose as ‘hooking’ people – enticing them into frequent or enduring usage. Inspired by steady references to capture in the field, the author considers recommender systems as traps, drawing on anthropological theories about animal trapping. The article charts the rise of ‘captivation metrics’ – measures of user retention – enabled by a set of transformations in recommenders’ epistemic, economic, and technical contexts. Traps prove useful for thinking about how such systems relate to broader infrastructural ecologies of knowledge and technology. As recommenders spread across online cultural infrastructures and become practically inescapable, thinking with traps offers an alternative to common ethical framings that oppose tropes of freedom and coercion.

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“…The value of personalization for listeners is integral to the challenges represented by commercial data mining (Goldschmitt & Seaver, 2019;Prey, 2019). People tacitly accept unprecedented tracking of online behaviour (Goldschmitt & Seaver, 2019, p. 67), and while tracking user data is used to optimize recommendations to listeners, these data insights also represent assets for streaming platforms (Drott, 2018;Prey, 2019).…”

Section: Algorithms and Discovery: Algorithmic Valuementioning

confidence: 99%

“…Insights into how people see, use and value music streaming services complement existing efforts to conceptualize how these services 'see' and categorize users. A common argument is that music streaming services need to find ways to differentiate their services given that the catalogue differences are minimal (Goldschmitt & Seaver, 2019;Morris & Powers, 2015;Prey, 2019). I do not, however, examine whether music streaming services are successful in their attempts to differentiate their brand from their competitors, but treat music streaming services generically, with the proposition that experienced value and loyalty are likely connected.…”

Section: Introductionmentioning

confidence: 98%

Ubiquitous tunes, virtuous archiving and catering for algorithms: the tethered affairs of people and music streaming services

Lüders

2020

Information, Communication & Society

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Music streaming services all provide affordable and easy access to massive databases of music and instead attempt to increase customer loyalty by optimizing personalized recommendations and offering opportunities for listeners to build their 'own' music libraries. In this paper, these features are operationalized as the convenience, price, archive and algorithmic value of streaming services. Existing studies detail the multitude of ways of making sense of these services, but do not allow for specifying what features matter more. Drawing on an online survey and interviews, this study indicates that price, convenience and archive value predict continued intention to use music streaming services. Survey results negate the importance of algorithmic value, but the interviews suggest that for some, algorithmic individuation is too evasive to be noticed. Those who 'see' and value recommendations 'cater for algorithms' and consider their own listening and archiving practices as input for optimizing output. Combined, the different features set the stage for both overt and covert tethering of people and streaming services.

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Shaping the Stream: Techniques and Troubles of Algorithmic Recommendation

Cited by 73 publications

References 190 publications

“Splendid Isolation”: The reproduction of music industry inequalities in Spotify’s recommendation system

“Splendid Isolation”: The reproduction of music industry inequalities in Spotify’s recommendation system

Captivating algorithms: Recommender systems as traps

Ubiquitous tunes, virtuous archiving and catering for algorithms: the tethered affairs of people and music streaming services

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