@proceedings{103386,
  author = {Allison J.B. Chaney and Brandon M. Stewart and Barbara E. Engelhardt},
  title = {How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility},
  abstract = {<p>Recommendation systems are ubiquitous and impact many domains; they have the potential to influence product consumption, individuals{\textquoteright} 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.</p>
},
  year = {2018},
  journal = {Twelfth ACM Conference on Recommender  Systems (RecSys {\textquoteright}18)},
  publisher = {ACM},
  address = {Vancouver, BC, Canada.},
  url = {https://arxiv.org/abs/1710.11214},
  language = {eng},
}