Deconstructing the Filter Bubble: User Decision-Making and Recommender Systems

Guy Aridor, Duarte Goncalves, Shan Sikdar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationRecSys 2020 - 14th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery, Inc
Pages82-91
Number of pages10
ISBN (Electronic)9781450375832
DOIs
StatePublished - Sep 22 2020
Event14th ACM Conference on Recommender Systems, RecSys 2020 - Virtual, Online, Brazil
Duration: Sep 22 2020Sep 26 2020

Publication series

NameRecSys 2020 - 14th ACM Conference on Recommender Systems

Conference

Conference14th ACM Conference on Recommender Systems, RecSys 2020
Country/TerritoryBrazil
CityVirtual, Online
Period9/22/209/26/20

Keywords

  • Filter Bubbles
  • Recommender Systems
  • Similarity-based Generalization

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Software
  • Computer Science Applications

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