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Research Details
Deconstructing the Filter Bubble: User Decision-Making and Recommender Systems, Proceedings of the ACM Conference on Recommender Systems
Abstract
We study a model of user decision-making in the context of recommender systems via numerical simulation. Our model provides an explanation for the findings of Nguyen, et. al (2014), where, in environments where recommender systems are typically deployed, users consume increasingly similar items over time even without recommendation. We find that recommendation alleviates these natural filter-bubble effects, but that it also leads to an increase in homogeneity across users, resulting in a trade-off between homogenizing across-user consumption and diversifying within-user consumption. Finally, we discuss how our model highlights the importance of collecting data on user beliefs and their evolution over time both to design better recommendations and to further understand their impact.
Type
Article
Author(s)
Guy Aridor, Duarte Goncalves, Shan Sikdar
Date Published
2020
Citations
Aridor, Guy, Duarte Goncalves, and Shan Sikdar. 2020. Deconstructing the Filter Bubble: User Decision-Making and Recommender Systems. Proceedings of the ACM Conference on Recommender Systems.