Captivating algorithms: Recommender systems as traps
Captivating algorithms: Recommender systems as traps
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迷人的算法：推荐系统作为陷阱
DOI：
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发表时间：
2018
影响因子：
0.9
通讯作者：
Nick Seaver
中科院分区：
文献类型：
--
作者：
Nick Seaver
算法推荐系统是当代在线文化生活中普遍存在的特征，它向用户推荐音乐、电影和其他材料。本文根据与美国推荐系统开发人员的实地调查，描述了这些系统制造商将其目的描述为“吸引”人们的趋势——吸引他们频繁或持久使用。受到野外捕捉的不断引用的启发，作者借鉴了有关动物诱捕的人类学理论，将推荐系统视为陷阱。这篇文章描绘了“吸引力指标”（衡量用户保留率的指标）的兴起，这是由推荐者的认知、经济和技术背景的一系列转变所促成的。事实证明，陷阱有助于思考此类系统如何与更广泛的知识和技术基础设施生态相关。随着推荐系统遍布在线文化基础设施并变得几乎不可避免，陷阱思维为反对自由和强制的常见道德框架提供了另一种选择。
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.
俘虏的客人：中国西南诺苏人的热情好客的蜘蛛网
影响因子：
1.2
作者：
SWANCUTT K
通讯作者：
SWANCUTT K