(University of Chicago Press, 2022)
In Computing Taste, Nick Seaver provides an ethnographic exploration of the world of music recommendation systems, revealing how algorithms are deeply shaped by the humans who design them. He shows how the algorithms that drive music recommendations are shaped by human judgment, creativity, and cultural assumptions. The data companies collect, the way they construct models, how they intuitively test whether their models are working, and how they define success are all deeply human and subjective choices.
Beyond Man vs. Machine
Seaver points out that textbook definitions describe algorithms as “well-defined computational procedures” that take inputs and generate outputs, portraying them as deterministic and straightforward systems. This narrow view leads to a man-versus-machine narrative that is trite and unilluminating. Treating algorithms as though their defining quality is the absence of human influence reinforces misconceptions about their neutrality. Instead, Seaver advocates for focusing on the sociotechnical arrangements that produce different forms of “humanness and machineness,” echoing observations by Donna Haraway and others.
In practice, algorithmic systems are messy, constantly evolving, and shaped by human judgment. As Seaver notes, “these ‘cultural’ details are technical details,” meaning that the motivations, preferences, and biases of the engineering teams that design algorithms are inseparable from the technical aspects of the systems themselves. Therefore, understanding algorithms requires acknowledging the social and cultural contexts in which they operate.
From Information Overload to Capture
Seaver shows how the objective of recommendation systems has shifted from the founding myth of information overload to the current obsession with capturing user attention. Pioneers of recommender systems told stories of information overload that presented growing consumer choice as a problem in need of a solution. The notion of overwhelming users with too much content has been a central justification for creating algorithms designed to filter and organize information. If users are helpless in the face of vast amounts of data, algorithms become necessary tools to help them navigate this digital landscape. Seaver argues that the framing of overload justifies the control algorithms exert over what users see, hear, and engage with. The idea of “too much music” or “too much content” becomes a convenient rationale for developing systems that, in practice, do more than assist—they guide, constrain, and shape user choices.
In any event, commercial imperatives soon led to rationales based on information overload giving way to narratives of capture. Seaver compares recommender systems to traps designed to “hook” users, analyzing how metrics such as engagement and retention guide the development of algorithms. Seaver traces the evolution of recommender systems from their origins as tools to help users navigate the overwhelming abundance of digital content to their current role in capturing and retaining user attention. The Netflix Prize, a 2006 competition aimed at improving Netflix’s recommendation algorithm, serves as a key example of this shift. Initially, algorithms were designed to help users manage “information overload” by personalizing content based on user preferences, as Netflix sought to predict what users would enjoy. However, Netflix never used the winning entry. As streaming services became central to Netflix’s business model, the focus of recommendation systems shifted from merely helping users find content to keeping them engaged on the platform for as long as possible. This transition from personalization to attention retention shows the shift in the industry’s goals. Recommender systems, including those at Netflix, began to focus on encouraging continuous engagement by suggesting binge-worthy content to maximize viewing hours, implementing autoplay features to keep the next episode or movie rolling without user interaction, and focusing on actual viewing habits (e.g., “skip intro” clicks, time spent on a show, completion rates) rather than ratings to keep users hooked.
Seaver’s perspective is insightful, not unrelentingly critical. The final chapter investigates how the design of recommendation systems reflects the metaphor of a “park”—a managed, curated space that users are guided through. Recommender systems are neither strictly benign nor malign, but they do entail a loss of user agency. We, the listening public, are not trapped animals so much as a managed flock. Seaver recognizes that recommendation systems open up new possibilities for exploration while also constraining user behavior by narrowing choices based on past preferences.
Why Do My Playlists Still Suck?
The book also answers the question that motivated me to read it: why do my playlists still suck? No one has a good model for why we like the music that we like, when we like it, or how that extrapolates to music we haven’t heard yet. And Spotify and other corporate interests have no real interest in solving that puzzle for us. The algorithms that shape our cultural lives now prioritize engagement, rely on past behavior, and reflect a grab bag of assumptions about user preferences that are often in conflict. There is very little upside to offering us fresh or risky suggestions when a loop of familiarity will keep us more reliably engaged.