How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases UtilityDownload PDFOpen Website

2017 (modified: 08 May 2023)CoRR 2017Readers: Everyone
Abstract: Recommendation systems are ubiquitous and impact many domains; they have the potential to influence product consumption, individuals' perceptions of the world, and life-altering decisions. These systems are often evaluated or trained with data from users already exposed to algorithmic recommendations; this creates a pernicious feedback loop. Using simulations, we demonstrate how using data confounded in this way homogenizes user behavior without increasing utility.
0 Replies

Loading