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In today’s digital age, music streaming services promise a personalized listening experience, but are they truly personal? In Nothing Personal: Algorithmic Individuation on Music Streaming Platforms (2017), Robert Prey dives into how platforms like Spotify and Pandora use algorithms to create tailored experiences while shaping our identities. Prey argues that while these services may seem to cater to individual tastes, they’re more about constructing individuals based on data, serving the interests of both the user and advertisers.
The Illusion of Personalization
Prey opens by challenging the notion of personalization, suggesting that users are not seen as individuals but as data points, molded by platforms through algorithms. He references Raymond Williams’ famous observation: “there are in fact no individuals, only ways of seeing people as individuals” (Prey, 2017, p. 1087). Streaming platforms use these “ways of seeing” to offer seemingly personalized music recommendations, but they ultimately shape user identities through commercial motives. Prey argues that platforms don’t just reflect our tastes; they play a critical role in constructing them based on the data we provide.
Spotify vs. Pandora: Competing Models of Individuation
Prey contrasts two of the largest music streaming platforms, Spotify and Pandora, highlighting their differing approaches to personalization. Pandora’s Music Genome Project focuses on intrinsic musical traits, presenting users with recommendations based on deep musical analysis. As Prey explains, Pandora “promises to serve you music that is individually – genetically – fitted to you” (Prey, 2017, p. 1090). On the other hand, Spotify’s Discover Weekly algorithm builds recommendations based on relational data, taking into account the preferences of other users with similar behaviors. Prey notes, “Discover Weekly combines both your personal taste in music with what others are playlisting” (Prey, 2017, p. 1091). Both systems, however, rely heavily on user interactions and behaviors to construct the “data subject.”
Algorithmic Individuation and Its Commercial Implications
Beyond just creating personalized music experiences, these platforms shape identities for the benefit of advertisers. Prey delves into how platforms like Spotify use user data to serve highly targeted ads, crafting identities based on behaviors, preferences, and context. For instance, ads may shift depending on whether the user is listening while jogging, driving, or relaxing. “Our online practices act as inputs according to which profiles are constructed,” Prey writes, “sorted into ‘measurable types’ such as ‘young African American’ or ‘female college-educated foreigner’” (Prey, 2017, p. 1089). This constant categorization creates a limited, commercialized version of the user, often reinforcing consumer behaviors that benefit the platforms’ advertisers.
Conclusion
Prey’s analysis provides a powerful reminder of the ways in which algorithm-driven platforms influence our identities. While these services seem to offer individualized experiences, they instead create commercial profiles based on our behaviors, molding our music preferences—and, by extension, our identities. The concept of “algorithmic individuation” reminds us that these platforms are not merely passive tools reflecting our desires but active agents shaping who we are. As Prey concludes, “On contemporary music streaming services, what our listening data say about us is fused with what it can infer about who we might be” (Prey, 2017, p. 1098). In the age of music streaming, it’s worth asking: are we truly being seen as individuals, or just as algorithmically constructed data subjects?
References
Prey, R. (2017). Nothing Personal: Algorithmic Individuation on Music Streaming Platforms. Media, Culture & Society, 40(7), 1086-1100. https://doi.org/10.1177/0163443717745147
