• Nick Seaver University of California, Irvine


Algorithmic recommendation systems are designed to aid users in their navigation of large catalogs of media, such as songs or movies. Among the developers of these systems, those catalogs are commonly referred to as constituting or occupying “spaces” — the "music space,” for example, might be the set of all music available to stream on Spotify, organized such that similar songs are near each other. The production of this space occupies much of the time of engineers who work on these systems, and although a mathematically defined space may sound neutral or objective with regard to the objects located in it, this work requires effectively arbitrary choices that are shaped by subjective interpretations, which in turn shape the spaces thus produced. In this paper, I draw on ethnographic fieldwork with the developers of algorithmic music recommender systems to describe some of the ways they make sense of this decision-making work amongst themselves. A common theme emerges in the use of landscape and agricultural metaphors: the makers of these systems describe themselves as “data gardeners,” tending to algorithmic outputs, or as “park rangers,” maintaining the grounds and helping visitors find their way. I argue that this imagery provides a middle route through two extreme positions regarding the origins of the “music space” — that it is an objectively discovered cultural order or that it is an interpretive invention of engineers. The language of landscape and agriculture places their work instead at the interface of the natural, cultural, and technical.
How to Cite
Seaver, N. (2018). PARKS AND RECOMMENDATION: SPATIAL IMAGINARIES IN ALGORITHMIC SYSTEMS. AoIR Selected Papers of Internet Research, 5. Retrieved from
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