Captivating algorithms: Recommender systems as traps. (December 2019)

• Record Type: Journal Article
• Authors: Seaver, Nick
• Other Names: Corsín Jiménez Alberto guest-editor.; Nahum-Claudel Chloe guest-editor.
• Is Part Of: Journal of material culture. Volume 24:Number 4(2019)
• Page Start: 421
• Page End: 436
• Publication Date: 2019-12
• Subjects: algorithms -- behaviorism -- infrastructure -- recommender systems -- traps; Material culture -- Periodicals; 306
• DOI: 10.1177/1359183518820366
• Languages: English
• ISSNs: 1359-1835

Abstract

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.