Algorithmic Assortative Matching on a Digital Social Medium

Online algorithms recommend “people we may know” and “content we may like.” Inherent in these recommendations is a notion of positive assortativity in which the people and content being suggested to us match our own preferences and beliefs. In this paper, we focus on such tacit (i.e., behind the sce...

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Veröffentlicht in:	Information systems research 2022-12, Vol.33 (4), p.1138-1156
Hauptverfasser:	López Vargas, Kristian, Runge, Julian, Zhang, Ruizhi
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms assortative matching field experiments freemium Machine learning Matching Social factors Social interaction social media Social networks systems design and implementation Teams User behavior Virtual environments Virtual reality
Online-Zugang:	Volltext
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