How algorithmic confounding in recommendation systems increases homogeneity and decreases utility

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.

• Publication year

  2017

• Venue

  ACM Conference on Recommender Systems

• Publication date

  2017-10-30

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1145/3240323.3240370arXiv 1710.11214
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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