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|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.|
|Citation:||Chaney, Allison JB, Brandon M. Stewart, and Barbara E. Engelhardt. "How algorithmic confounding in recommendation systems increases homogeneity and decreases utility." In Proceedings of the 12th ACM Conference on Recommender Systems (2018): pp. 224-232. doi:10.1145/3240323.3240370|
|Pages:||224 - 232|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||Proceedings of the 12th ACM Conference on Recommender Systems|
|Version:||Final published version. This is an open access article.|
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