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Making it personal [Elektronski vir] : algorithmic personalization, identity, and everyday lifeKant, TanyaThe encounter of "personalized experiences"-targeted advertisements, tailored information feeds and "recommended" content among other things-is now a common and somewhat inescapable component of ... digital life. More often than not however, "you" the user are not primarily responsible for personalizing your web engagements: instead, with the help of your search, browsing, and purchase histories, your "likes", your click-throughs, and a multitude of other data you produce as you go about your day, your experience can "conveniently"-and computationally-be personalized on your behalf. This book explores a host of new questions that emerge from web users' encounters with these forms of algorithmic personalization. What do users "know" about the algorithms that apparently "know" them? If personalization practices seek to act on the user's behalf (for instance by deciding what is content is personally relevant), then how do users retain or relinquish their autonomy? Indeed, what kinds of selfhoods are made possible when personalization algorithms intervene in identity construction? Making it Personal is the first full-length monograph to critically analyze the socio-cultural implications of algorithmic personalization through the accounts and testimonies of web users themselves. At the heart of the book are interviews and focus groups with web users who-through a myriad of resistant, tactical, resigned or trusting engagements-encounter algorithmic personalization as part of their lived experience on the web. The book proposes that for those who encounter it, algorithmic personalization creates new implications for knowledge production, autonomy, cultural capital, and formations of self.Type of material - e-book ; adult, seriousPublication and manufacture - New York : Oxford University Press, cop. 2020Language - englishISBN - 978-0-19-090511-8; 978-0-19-090512-5; 978-0-19-090510-1COBISS.SI-ID - 48046339
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Kant, Tanya | ![]() |
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